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200 | Improving the Training of Data-Efficient GANs via Quality Aware Dynamic Discriminator Rejection Sampling | [
"Zhaoyu Zhang",
"Yang Hua",
"Guanxiong Sun",
"Hui Wang",
"Seán McLoone"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Improving_the_Training_of_Data-Efficient_GANs_via_Quality_Aware_Dynamic_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Improving_the_Training_of_Data-Efficient_GANs_via_Quality_Aware_Dynamic_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Improving_the_Training_CVPR_2025_supplemental.pdf | null | @InProceedings{Zhang_2025_CVPR,
author = {Zhang, Zhaoyu and Hua, Yang and Sun, Guanxiong and Wang, Hui and McLoone, Se\'an},
title = {Improving the Training of Data-Efficient GANs via Quality Aware Dynamic Discriminator Rejection Sampling},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30682-30691}
} | Data-Efficient Generative Adversarial Nets (DE-GANs) have become more and more popular in recent years. Existing methods apply data augmentation, noise injection and pre-trained models to maximumly increase the number of training samples thus improving the training of DE-GANs. However, none of these methods considers the sample quality during training, which can also significantly influence the training of DE-GANs. Focusing on sample quality during training, in this paper, we are the first to incorporate discriminator rejection sampling (DRS) into the training process and introduce a novel method, called quality aware dynamic discriminator rejection sampling (QADDRS). Specifically, QADDRS consists of two steps: (1) the sample quality aware step, which aims to obtain the sorted critic scores, i.e., the ordered discriminator outputs, on real/fake samples in the current training stage; (2) the dynamic rejection step that obtains dynamic rejection number N, where N is controlled by the overfitting degree of discriminator (D) during training. When updating the parameters of D, the N high critic score real samples and the N low critic score fake samples in the minibatch are rejected dynamically based on the overfitting degree of D. As a result, QADDRS can avoid D becoming overly confident in distinguishing both real and fake samples, thereby alleviating the overfitting of D issue during training. Extensive experiments on several datasets demonstrate that integrating QADDRS into different DE-GANs can achieve better performance and deliver state-of-the-art results. Codes are available at https://github.com/zzhang05/QADDRS. | [
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] |
201 | Shading Meets Motion: Self-supervised Indoor 3D Reconstruction Via Simultaneous Shape-from-Shading and Structure-from-Motion | [
"Guoyu Lu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lu_Shading_Meets_Motion_Self-supervised_Indoor_3D_Reconstruction_Via_Simultaneous_Shape-from-Shading_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_Shading_Meets_Motion_Self-supervised_Indoor_3D_Reconstruction_Via_Simultaneous_Shape-from-Shading_CVPR_2025_paper.pdf | null | null | @InProceedings{Lu_2025_CVPR,
author = {Lu, Guoyu},
title = {Shading Meets Motion: Self-supervised Indoor 3D Reconstruction Via Simultaneous Shape-from-Shading and Structure-from-Motion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16508-16519}
} | Scene reconstruction has a wide range of applications in computer vision and robotics. To build practical constraints and feature Scene reconstruction has a wide range of applications in computer vision and robotics. To build practical constraints and feature correspondences, rich textures and distinguished gradient variations are particularly required in classic and learning-based SfM. When building low-texture regions with repeated patterns, especially mostly-white indoor rooms, there is a significant drop in performance. In this work, we propose Shading-SfM-Net, a novel framework for simultaneously learning a shape-from-shading network based on the inverse rendering constraint and a structure-from-motion framework based on warped keypoint and geometric consistency, to improve structure-from-motion and surface reconstruction for low-texture indoor scenes. Shading-SfM-Net tightly incorporates the surface shape consistency and 3D geometric registration loss in order to dig into their mutual information and further overcome the instability on flat regions. We evaluate the proposed framework on texture-less indoor scenes (NYUv2 and ScanNet), and show that by simultaneously learning shading, motion and shape, our pipeline is able to achieve state-of-the-art performance with superior generalization capability for unseen texture-less datasets. | [
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] |
202 | Believing is Seeing: Unobserved Object Detection using Generative Models | [
"Subhransu S. Bhattacharjee",
"Dylan Campbell",
"Rahul Shome"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Bhattacharjee_Believing_is_Seeing_Unobserved_Object_Detection_using_Generative_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Bhattacharjee_Believing_is_Seeing_Unobserved_Object_Detection_using_Generative_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bhattacharjee_Believing_is_Seeing_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2410.05869 | @InProceedings{Bhattacharjee_2025_CVPR,
author = {Bhattacharjee, Subhransu S. and Campbell, Dylan and Shome, Rahul},
title = {Believing is Seeing: Unobserved Object Detection using Generative Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19366-19377}
} | Can objects that are not visible in an image---but are in the vicinity of the camera---be detected? This study introduces the novel tasks of 2D, 2.5D and 3D unobserved object detection for predicting the location of nearby objects that are occluded or lie outside the image frame. We adapt several state-of-the-art pre-trained generative models to address this task, including 2D and 3D diffusion models and vision-language models, and show that they can be used to infer the presence of objects that are not directly observed. To benchmark this task, we propose a suite of metrics that capture different aspects of performance. Our empirical evaluation on indoor scenes from the RealEstate10k and NYU Depth v2 datasets demonstrate results that motivate the use of generative models for the unobserved object detection task. | [
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203 | MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation | [
"Shuwei Shi",
"Biao Gong",
"Xi Chen",
"Dandan Zheng",
"Shuai Tan",
"Zizheng Yang",
"Yuyuan Li",
"Jingwen He",
"Kecheng Zheng",
"Jingdong Chen",
"Ming Yang",
"Yinqiang Zheng"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Shi_MotionStone_Decoupled_Motion_Intensity_Modulation_with_Diffusion_Transformer_for_Image-to-Video_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_MotionStone_Decoupled_Motion_Intensity_Modulation_with_Diffusion_Transformer_for_Image-to-Video_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_MotionStone_Decoupled_Motion_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.05848 | @InProceedings{Shi_2025_CVPR,
author = {Shi, Shuwei and Gong, Biao and Chen, Xi and Zheng, Dandan and Tan, Shuai and Yang, Zizheng and Li, Yuyuan and He, Jingwen and Zheng, Kecheng and Chen, Jingdong and Yang, Ming and Zheng, Yinqiang},
title = {MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22864-22874}
} | The image-to-video (I2V) generation is conditioned on the static image, which has been enhanced recently by the motion intensity as an additional control signal. These motion-aware models are appealing to generate diverse motion patterns, yet there lacks a reliable motion estimator for training such models on large-scale video set in the wild. Traditional metrics, e.g., SSIM or optical flow, are hard to generalize to arbitrary videos, while, it is very tough for human annotators to label the abstract motion intensity neither. Furthermore, the motion intensity shall reveal both local object motion and global camera movement, which has not been studied before. This paper addresses the challenge with a new motion estimator, capable of measuring the decoupled motion intensities of objects and cameras in video. We leverage the contrastive learning on randomly paired videos and distinguish the video with greater motion intensity. Such a paradigm is friendly for annotation and easy to scale up to achieve stable performance on motion estimation. We then present a new I2V model, named MotionStone, developed with the decoupled motion estimator. Experimental results demonstrate the stability of the proposed motion estimator and the state-of-the-art performance of MotionStone on I2V generation. These advantages warrant the decoupled motion estimator to serve as a general plug-in enhancer for both data processing and video generation training. | [
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204 | NLPrompt: Noise-Label Prompt Learning for Vision-Language Models | [
"Bikang Pan",
"Qun Li",
"Xiaoying Tang",
"Wei Huang",
"Zhen Fang",
"Feng Liu",
"Jingya Wang",
"Jingyi Yu",
"Ye Shi"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Pan_NLPrompt_Noise-Label_Prompt_Learning_for_Vision-Language_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Pan_NLPrompt_Noise-Label_Prompt_Learning_for_Vision-Language_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pan_NLPrompt_Noise-Label_Prompt_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.01256 | @InProceedings{Pan_2025_CVPR,
author = {Pan, Bikang and Li, Qun and Tang, Xiaoying and Huang, Wei and Fang, Zhen and Liu, Feng and Wang, Jingya and Yu, Jingyi and Shi, Ye},
title = {NLPrompt: Noise-Label Prompt Learning for Vision-Language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19963-19973}
} | The emergence of vision-language foundation models, such as CLIP, has revolutionized image-text representation, enabling a broad range of applications via prompt learning. Despite its promise, real-world datasets often contain noisy labels that can degrade prompt learning performance. In this paper, we demonstrate that using mean absolute error (MAE) loss in prompt learning, named PromptMAE, significantly enhances robustness against noisy labels while maintaining high accuracy. Though MAE is straightforward and recognized for its robustness, it is rarely used in noisy-label learning due to its slow convergence and poor performance outside prompt learning scenarios. To elucidate the robustness of PromptMAE, we leverage feature learning theory to show that MAE can suppress the influence of noisy samples, thereby improving the signal-to-noise ratio and enhancing overall robustness. Additionally, we introduce PromptOT, a prompt-based optimal transport data purification method to enhance the robustness further. PromptOT employs text encoder representations in vision-language models as prototypes to construct an optimal transportation matrix. This matrix effectively partitions datasets into clean and noisy subsets, allowing for the application of cross-entropy loss to the clean subset and MAE loss to the noisy subset. Our Noise-Label Prompt Learning method, named NLPrompt, offers a simple and efficient approach that leverages the expressive representation and precise alignment capabilities of vision-language models for robust prompt learning. We validate NLPrompt through extensive experiments across various noise settings, demonstrating significant performance improvements. | [
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205 | MEGA: Masked Generative Autoencoder for Human Mesh Recovery | [
"Guénolé Fiche",
"Simon Leglaive",
"Xavier Alameda-Pineda",
"Francesc Moreno-Noguer"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Fiche_MEGA_Masked_Generative_Autoencoder_for_Human_Mesh_Recovery_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Fiche_MEGA_Masked_Generative_Autoencoder_for_Human_Mesh_Recovery_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fiche_MEGA_Masked_Generative_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2405.18839 | @InProceedings{Fiche_2025_CVPR,
author = {Fiche, Gu\'enol\'e and Leglaive, Simon and Alameda-Pineda, Xavier and Moreno-Noguer, Francesc},
title = {MEGA: Masked Generative Autoencoder for Human Mesh Recovery},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {5366-5378}
} | Human Mesh Recovery (HMR) from a single RGB image is a highly ambiguous problem, as an infinite set of 3D interpretations can explain the 2D observation equally well. Nevertheless, most HMR methods overlook this issue and make a single prediction without accounting for this ambiguity. A few approaches generate a distribution of human meshes, enabling the sampling of multiple predictions; however, none of them is competitive with the latest single-output model when making a single prediction. This work proposes a new approach based on masked generative modeling. By tokenizing the human pose and shape, we formulate the HMR task as generating a sequence of discrete tokens conditioned on an input image. We introduce MEGA, a MaskEd Generative Autoencoder trained to recover human meshes from images and partial human mesh token sequences. Given an image, our flexible generation scheme allows us to predict a single human mesh in deterministic mode or to generate multiple human meshes in stochastic mode. Experiments on in-the-wild benchmarks show that MEGA achieves state-of-the-art performance in deterministic and stochastic modes, outperforming single-output and multi-output approaches. | [
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] |
206 | PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields | [
"Sean Wu",
"Shamik Basu",
"Tim Broedermann",
"Luc Van Gool",
"Christos Sakaridis"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wu_PBR-NeRF_Inverse_Rendering_with_Physics-Based_Neural_Fields_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_PBR-NeRF_Inverse_Rendering_with_Physics-Based_Neural_Fields_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_PBR-NeRF_Inverse_Rendering_CVPR_2025_supplemental.pdf | null | @InProceedings{Wu_2025_CVPR,
author = {Wu, Sean and Basu, Shamik and Broedermann, Tim and Van Gool, Luc and Sakaridis, Christos},
title = {PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10974-10984}
} | We tackle the ill-posed inverse rendering problem in 3D reconstruction with a Neural Radiance Field (NeRF) approach informed by Physics-Based Rendering (PBR) theory, named PBR-NeRF. Our method addresses a key limitation in most NeRF and 3D Gaussian Splatting approaches: they estimate view-dependent appearance without modeling scene materials and illumination. To address this limitation, we present an inverse rendering (IR) model capable of jointly estimating scene geometry, materials, and illumination. Our model builds upon recent NeRF-based IR approaches, but crucially introduces two novel physics-based priors that better constrain the IR estimation. Our priors are rigorously formulated as intuitive loss terms and achieve state-of-the-art material estimation without compromising novel view synthesis quality. Our method is easily adaptable to other inverse rendering and 3D reconstruction frameworks that require material estimation. We demonstrate the importance of extending current neural rendering approaches to fully model scene properties beyond geometry and view-dependent appearance. Code is publicly available at: https://github.com/s3anwu/pbrnerf. | [
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207 | Disentangling Safe and Unsafe Image Corruptions via Anisotropy and Locality | [
"Ramchandran Muthukumar",
"Ambar Pal",
"Jeremias Sulam",
"Rene Vidal"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Muthukumar_Disentangling_Safe_and_Unsafe_Image_Corruptions_via_Anisotropy_and_Locality_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Muthukumar_Disentangling_Safe_and_Unsafe_Image_Corruptions_via_Anisotropy_and_Locality_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Muthukumar_Disentangling_Safe_and_CVPR_2025_supplemental.pdf | null | @InProceedings{Muthukumar_2025_CVPR,
author = {Muthukumar, Ramchandran and Pal, Ambar and Sulam, Jeremias and Vidal, Rene},
title = {Disentangling Safe and Unsafe Image Corruptions via Anisotropy and Locality},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9954-9963}
} | State-of-the-art machine learning systems are vulnerable to small perturbations to their input, where _small_ is defined according to a threat model that assigns a positive threat to each perturbation. Most prior works define a task-agnostic, isotropic, and global threat, like the l_p norm, where the magnitude of the perturbation fully determines the degree of the threat and neither the direction of the attack nor its position in space matter. However, common corruptions in computer vision, such as blur, compression, or occlusions, are not well captured by such treat models. This paper proposes a novel threat model called \texttt Projected Displacement (PD) to study robustness beyond existing isotropic and global threat models. The proposed threat model measures the threat of a perturbation via its alignment with _unsafe directions_, defined as directions in the input space along which a perturbation of sufficient magnitude changes the ground truth class label. Unsafe directions are identified locally for each input based on observed training data. In this way, the PD-threat model exhibits anisotropy and locality. The PD-threat model is computationally efficient and can be easily integrated into existing robustness pipelines. Experiments on Imagenet-1k data indicate that, for any input, the set of perturbations with small PD threat includes _safe_ perturbations of large l_p norm that preserve the true label, such as noise, blur and compression, while simultaneously excluding _unsafe_ perturbations that alter the true label. Unlike perceptual threat models based on embeddings of large-vision models, the PD-threat model can be readily computed for arbitrary classification tasks without pre-training or finetuning. Further additional task information such as sensitivity to image regions or concept hierarchies can be easily integrated into the assessment of threat and thus the PD threat model presents practitioners a flexible, task-driven threat specification that alleviates the limitations of l_p-threat models. | [
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208 | Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation | [
"Yuanbo Yang",
"Jiahao Shao",
"Xinyang Li",
"Yujun Shen",
"Andreas Geiger",
"Yiyi Liao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Prometheus_3D-Aware_Latent_Diffusion_Models_for_Feed-Forward_Text-to-3D_Scene_Generation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Prometheus_3D-Aware_Latent_Diffusion_Models_for_Feed-Forward_Text-to-3D_Scene_Generation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Prometheus_3D-Aware_Latent_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.21117 | @InProceedings{Yang_2025_CVPR,
author = {Yang, Yuanbo and Shao, Jiahao and Li, Xinyang and Shen, Yujun and Geiger, Andreas and Liao, Yiyi},
title = {Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2857-2869}
} | In this work, we introduce Prometheus, a 3D-aware latent diffusion model for text-to-3D generation at both object and scene levels in seconds. We formulate 3D scene generation as multi-view, feed-forward, pixel-aligned 3D Gaussian generation within the latent diffusion paradigm. To ensure generalizability, we build our model upon pre-trained text-to-image generation model with only minimal adjustments and further train it using a large number of images from both single-view and multi-view datasets. Furthermore, we introduce an RGB-D latent space into 3D Gaussian generation to disentangle appearance and geometry information, enabling efficient feed-forward generation of 3D Gaussians with better fidelity and geometry. Extensive experimental results demonstrate the effectiveness of our method in both feed-forward 3D Gaussian reconstruction and text-to-3D generation. | [
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] |
209 | No Pains, More Gains: Recycling Sub-Salient Patches for Efficient High-Resolution Image Recognition | [
"Rong Qin",
"Xin Liu",
"Xingyu Liu",
"Jiaxuan Liu",
"Jinglei Shi",
"Liang Lin",
"Jufeng Yang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Qin_No_Pains_More_Gains_Recycling_Sub-Salient_Patches_for_Efficient_High-Resolution_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Qin_No_Pains_More_Gains_Recycling_Sub-Salient_Patches_for_Efficient_High-Resolution_CVPR_2025_paper.pdf | null | null | @InProceedings{Qin_2025_CVPR,
author = {Qin, Rong and Liu, Xin and Liu, Xingyu and Liu, Jiaxuan and Shi, Jinglei and Lin, Liang and Yang, Jufeng},
title = {No Pains, More Gains: Recycling Sub-Salient Patches for Efficient High-Resolution Image Recognition},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14965-14975}
} | Over the last decade, many notable methods have emerged to tackle the computational resource challenge of the high resolution image recognition (HRIR). They typically focus on identifying and aggregating a few salient regions for classification, discarding sub-salient areas for low training consumption. Nevertheless, many HRIR tasks necessitate the exploration of wider regions to model objects and contexts, which limits their performance in such scenarios. To address this issue, we present a DBPS strategy to enable training with more patches at low consumption. Specifically, in addition to a fundamental buffer that stores the embeddings of most salient patches, DBPS further employs an auxiliary buffer to recycle those sub-salient ones. To reduce the computational cost associated with gradients of sub-salient patches, these patches are primarily used in the forward pass to provide sufficient information for classification. Meanwhile, only the gradients of the salient patches are back-propagated to update the entire network. Moreover, we design a Multiple Instance Learning (MIL) architecture that leverages aggregated information from salient patches to filter out uninformative background within sub-salient patches for better accuracy. Besides, we introduce the random patch drop to accelerate training process and uncover informative regions. Experiment results demonstrate the superiority of our method in terms of both accuracy and training consumption against other advanced methods. The code is available in the https://github.com/Qinrong-NKU/DBPS. | [
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] |
210 | SphereUFormer: A U-Shaped Transformer for Spherical 360 Perception | [
"Yaniv Benny",
"Lior Wolf"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Benny_SphereUFormer_A_U-Shaped_Transformer_for_Spherical_360_Perception_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Benny_SphereUFormer_A_U-Shaped_Transformer_for_Spherical_360_Perception_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Benny_SphereUFormer_A_U-Shaped_CVPR_2025_supplemental.pdf | null | @InProceedings{Benny_2025_CVPR,
author = {Benny, Yaniv and Wolf, Lior},
title = {SphereUFormer: A U-Shaped Transformer for Spherical 360 Perception},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {940-950}
} | This paper proposes a novel method for omnidirectional 360\degree perception. Most common previous methods relied on equirectangular projection. This representation is easily applicable to 2D operation layers but introduces distortions into the image. Other methods attempted to remove the distortions by maintaining a sphere representation but relied on complicated convolution kernels that failed to show competitive results. In this work, we introduce a transformer-based architecture that, by incorporating a novel "Spherical Local Self-Attention" and other spherically-oriented modules, successfully operates in the spherical domain and outperforms the state-of-the-art in 360\degree perception benchmarks for depth estimation and semantic segmentation. Our code is available at https://github.com/yanivbenny/sphere_uformer. | [
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] |
211 | Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models | [
"Yankai Jiang",
"Peng Zhang",
"Donglin Yang",
"Yuan Tian",
"Hai Lin",
"Xiaosong Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Advancing_Generalizable_Tumor_Segmentation_with_Anomaly-Aware_Open-Vocabulary_Attention_Maps_and_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Advancing_Generalizable_Tumor_Segmentation_with_Anomaly-Aware_Open-Vocabulary_Attention_Maps_and_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Advancing_Generalizable_Tumor_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2505.02753 | @InProceedings{Jiang_2025_CVPR,
author = {Jiang, Yankai and Zhang, Peng and Yang, Donglin and Tian, Yuan and Lin, Hai and Wang, Xiaosong},
title = {Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25971-25981}
} | We explore Generalizable Tumor Segmentation, aiming to train a single model for zero-shot tumor segmentation across diverse anatomical regions. Existing methods face limitations related to segmentation quality, scalability, and the range of applicable imaging modalities. In this paper, we uncover the potential of the internal representations within frozen medical foundation diffusion models as highly efficient zero-shot learners for tumor segmentation by introducing a novel framework named DiffuGTS. DiffuGTS creates anomaly-aware open-vocabulary attention maps based on text prompts to enable generalizable anomaly segmentation without being restricted by a predefined training category list. To further improve and refine anomaly segmentation masks, DiffuGTS leverages the diffusion model, transforming pathological regions into high-quality pseudo-healthy counterparts through latent space inpainting, and applies a novel pixel-level and feature-level residual learning approach, resulting in segmentation masks with significantly enhanced quality and generalization. Comprehensive experiments on four datasets and seven tumor categories demonstrate the superior performance of our method, surpassing current state-of-the-art models across multiple zero-shot settings. Codes are available at https://github.com/Yankai96/DiffuGTS. | [
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] |
212 | Towards Generalizable Scene Change Detection | [
"Jae-Woo Kim",
"Ue-Hwan Kim"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Towards_Generalizable_Scene_Change_Detection_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Towards_Generalizable_Scene_Change_Detection_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Towards_Generalizable_Scene_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2409.06214 | @InProceedings{Kim_2025_CVPR,
author = {Kim, Jae-Woo and Kim, Ue-Hwan},
title = {Towards Generalizable Scene Change Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24463-24473}
} | While current state-of-the-art Scene Change Detection (SCD) approaches achieve impressive results in well-trained research data, they become unreliable under unseen environments and different temporal conditions; in-domain performance drops from 77.6% to 8.0% in a previously unseen environment and to 4.6% under a different temporal condition---calling for generalizable SCD and benchmark. In this work, we propose the Generalizable Scene Change Detection Framework (GeSCF), which addresses unseen domain performance and temporal consistency---to meet the growing demand for anything SCD. Our method leverages the pre-trained Segment Anything Model (SAM) in a zero-shot manner. For this, we design Initial Pseudo-mask Generation and Geometric-Semantic Mask Matching---seamlessly turning user-guided prompt and single-image based segmentation into scene change detection for a pair of inputs without guidance. Furthermore, we define the Generalizable Scene Change Detection (GeSCD) benchmark along with novel metrics and an evaluation protocol to facilitate SCD research in generalizability. In the process, we introduce the ChangeVPR dataset, a collection of challenging image pairs with diverse environmental scenarios---including urban, suburban, and rural settings. Extensive experiments across various datasets demonstrate that GeSCF achieves an average performance gain of 19.2% on existing SCD datasets and 30.0% on the ChangeVPR dataset, nearly doubling the prior art performance. We believe our work can lay a solid foundation for robust and generalizable SCD research. | [
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] |
213 | Beyond Clean Training Data: A Versatile and Model-Agnostic Framework for Out-of-Distribution Detection with Contaminated Training Data | [
"Yuchuan Li",
"Jae-Mo Kang",
"Il-Min Kim"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Li_Beyond_Clean_Training_Data_A_Versatile_and_Model-Agnostic_Framework_for_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Beyond_Clean_Training_Data_A_Versatile_and_Model-Agnostic_Framework_for_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Beyond_Clean_Training_CVPR_2025_supplemental.pdf | null | @InProceedings{Li_2025_CVPR,
author = {Li, Yuchuan and Kang, Jae-Mo and Kim, Il-Min},
title = {Beyond Clean Training Data: A Versatile and Model-Agnostic Framework for Out-of-Distribution Detection with Contaminated Training Data},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10183-10192}
} | In real-world AI applications, training datasets are often contaminated, containing a mix of in-distribution (ID) and out-of-distribution (OOD) samples without labels. This contamination poses a significant challenge for developing and training OOD detection models, as nearly all existing methods assume access to a clean training dataset of only ID samples--a condition rarely met in real-world scenarios. Customizing each existing OOD detection method to handle such contamination is impractical, given the vast number of diverse methods designed for clean data. To address this issue, we propose a universal, model-agnostic framework that integrates with nearly all existing OOD detection methods, enabling training on contaminated datasets while achieving high OOD detection accuracy on test datasets. Additionally, our framework provides an accurate estimation of the unknown proportion of OOD samples within the training dataset--an important and distinct challenge in its own right. Our approach introduces a novel dynamic weighting function and transition mechanism within an iterative training structure, enabling both reliable estimation of the OOD sample proportion of the training data and precise OOD detection on test data. Extensive evaluations across diverse datasets, including ImageNet-1k, demonstrate that our framework accurately estimates OOD sample proportions of training data and substantially enhances OOD detection accuracy on test data. | [
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214 | Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space Model | [
"Zheyu Zhang",
"Yayuan Lu",
"Feipeng Ma",
"Yueyi Zhang",
"Huanjing Yue",
"Xiaoyan Sun"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Incomplete_Multi-modal_Brain_Tumor_Segmentation_via_Learnable_Sorting_State_Space_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Incomplete_Multi-modal_Brain_Tumor_Segmentation_via_Learnable_Sorting_State_Space_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Incomplete_Multi-modal_Brain_CVPR_2025_supplemental.pdf | null | @InProceedings{Zhang_2025_CVPR,
author = {Zhang, Zheyu and Lu, Yayuan and Ma, Feipeng and Zhang, Yueyi and Yue, Huanjing and Sun, Xiaoyan},
title = {Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space Model},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25982-25992}
} | Brain tumor segmentation plays a crucial role in clinical diagnosis, yet the frequent unavailability of certain MRI modalities poses a significant challenge. In this paper, we introduce the Learnable Sorting State Space Model (LS3M), a novel framework designed to maximize the utilization of available modalities for brain tumor segmentation. LS3M excels at efficiently modeling long-range dependencies based on the Mamba design, while incorporating differentiable permutation matrices that reorder input sequences based on modality-specific characteristics. This dynamic reordering ensures that critical spatial inductive biases and long-range semantic correlations inherent in 3D brain MRI are preserved, which is crucial for imcomplete multi-modal brain tumor segmentation.Once the input sequences are reordered using the generated permutation matrix, the Series State Space Model (S3M) block models the relationships between them, capturing both local and long-range dependencies. This enables effective representation of intra-modal and inter-modal relationships, significantly improving segmentation accuracy. Extensive experiments on the BraTS2018 and BraTS2020 datasets demonstrate that LS3M outperforms existing methods, offering a robust solution for brain tumor segmentation, particularly in scenarios with missing modalities. | [
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215 | FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors | [
"Changlong Shi",
"He Zhao",
"Bingjie Zhang",
"Mingyuan Zhou",
"Dandan Guo",
"Yi Chang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Shi_FedAWA_Adaptive_Optimization_of_Aggregation_Weights_in_Federated_Learning_Using_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_FedAWA_Adaptive_Optimization_of_Aggregation_Weights_in_Federated_Learning_Using_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_FedAWA_Adaptive_Optimization_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.15842 | @InProceedings{Shi_2025_CVPR,
author = {Shi, Changlong and Zhao, He and Zhang, Bingjie and Zhou, Mingyuan and Guo, Dandan and Chang, Yi},
title = {FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30651-30660}
} | Federated Learning (FL) has emerged as a promising framework for distributed machine learning, enabling collaborative model training without sharing local data, thereby preserving privacy and enhancing security. However, data heterogeneity resulting from differences across user behaviors, preferences, and device characteristics poses a significant challenge for federated learning. Most previous works overlook the adjustment of aggregation weights, relying solely on dataset size for weight assignment, which often leads to unstable convergence and reduced model performance. Recently, several studies have sought to refine aggregation strategies by incorporating dataset characteristics and model alignment. However, adaptively adjusting aggregation weights while ensuring data security--without requiring additional proxy data--remains a significant challenge. In this work, we propose Federated learning with Adaptive Weight Aggregation (FedAWA), a novel method that adaptively adjusts aggregation weights based on client vectors during the learning process. The client vector captures the direction of model updates, reflecting local data variations, and is used to optimize the aggregation weight without requiring additional datasets or violating privacy. By assigning higher aggregation weights to local models whose updates align closely with the global optimization direction, FedAWA enhances the stability and generalization of the global model. Extensive experiments under diverse scenarios demonstrate the superiority of our method, providing a promising solution to the challenges of data heterogeneity in federated learning. | [
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] |
216 | FreeUV: Ground-Truth-Free Realistic Facial UV Texture Recovery via Cross-Assembly Inference Strategy | [
"Xingchao Yang",
"Takafumi Taketomi",
"Yuki Endo",
"Yoshihiro Kanamori"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yang_FreeUV_Ground-Truth-Free_Realistic_Facial_UV_Texture_Recovery_via_Cross-Assembly_Inference_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_FreeUV_Ground-Truth-Free_Realistic_Facial_UV_Texture_Recovery_via_Cross-Assembly_Inference_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_FreeUV_Ground-Truth-Free_Realistic_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.17197 | @InProceedings{Yang_2025_CVPR,
author = {Yang, Xingchao and Taketomi, Takafumi and Endo, Yuki and Kanamori, Yoshihiro},
title = {FreeUV: Ground-Truth-Free Realistic Facial UV Texture Recovery via Cross-Assembly Inference Strategy},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {326-337}
} | Recovering high-quality 3D facial textures from single-view 2D images is a challenging task, especially under constraints of limited data and complex facial details such as makeup, wrinkles, and occlusions. In this paper, we introduce FreeUV, a novel ground-truth-free UV texture recovery framework that eliminates the need for annotated or synthetic UV data. FreeUV leverages pre-trained stable diffusion model alongside a Cross-Assembly inference strategy to fulfill this objective. In FreeUV, separate networks are trained independently to focus on realistic appearance and structural consistency, and these networks are combined during inference to generate coherent textures. Our approach accurately captures intricate facial features and demonstrates robust performance across diverse poses and occlusions. Extensive experiments validate FreeUV's effectiveness, with results surpassing state-of-the-art methods in both quantitative and qualitative metrics. Additionally, FreeUV enables new applications, including local editing, facial feature interpolation, and multi-view texture recovery. By reducing data requirements, FreeUV offers a scalable solution for generating high-fidelity 3D facial textures suitable for real-world scenarios. | [
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] |
217 | HarmonySet: A Comprehensive Dataset for Understanding Video-Music Semantic Alignment and Temporal Synchronization | [
"Zitang Zhou",
"Ke Mei",
"Yu Lu",
"Tianyi Wang",
"Fengyun Rao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_HarmonySet_A_Comprehensive_Dataset_for_Understanding_Video-Music_Semantic_Alignment_and_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_HarmonySet_A_Comprehensive_Dataset_for_Understanding_Video-Music_Semantic_Alignment_and_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_HarmonySet_A_Comprehensive_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.01725 | @InProceedings{Zhou_2025_CVPR,
author = {Zhou, Zitang and Mei, Ke and Lu, Yu and Wang, Tianyi and Rao, Fengyun},
title = {HarmonySet: A Comprehensive Dataset for Understanding Video-Music Semantic Alignment and Temporal Synchronization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3152-3162}
} | This paper introduces HarmonySet, a comprehensive dataset designed to advance video-music understanding. HarmonySet consists of 48,328 diverse video-music pairs, annotated with detailed information on rhythmic synchronization, emotional alignment, thematic coherence, and cultural relevance. We propose a multi-step human-machine collaborative framework for efficient annotation, combining human insights with machine-generated descriptions to identify key transitions and assess alignment across multiple dimensions. Additionally, we introduce a novel evaluation framework with tasks and metrics to assess the multi-dimensional alignment of video and music, including rhythm, emotion, theme, and cultural context. Our extensive experiments demonstrate that HarmonySet, along with the proposed evaluation framework, significantly improves the ability of multimodal models to capture and analyze the intricate relationships between video and music. Project page: https://harmonyset.github.io/. | [
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218 | Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregression | [
"Zichong Meng",
"Yiming Xie",
"Xiaogang Peng",
"Zeyu Han",
"Huaizu Jiang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Meng_Rethinking_Diffusion_for_Text-Driven_Human_Motion_Generation_Redundant_Representations_Evaluation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Meng_Rethinking_Diffusion_for_Text-Driven_Human_Motion_Generation_Redundant_Representations_Evaluation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Meng_Rethinking_Diffusion_for_CVPR_2025_supplemental.zip | null | @InProceedings{Meng_2025_CVPR,
author = {Meng, Zichong and Xie, Yiming and Peng, Xiaogang and Han, Zeyu and Jiang, Huaizu},
title = {Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregression},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27859-27871}
} | Since 2023, Vector Quantization (VQ)-based discrete generation methods have rapidly dominated human motion generation, primarily surpassing diffusion-based continuous generation methods in standard performance metrics. However, VQ-based methods have inherent limitations. Representing continuous motion data as limited discrete tokens leads to inevitable information loss, reduces the diversity of generated motions, and restricts their ability to function effectively as motion priors or generation guidance. In contrast, the continuous space generation nature of diffusion-based methods makes them well-suited to address these limitations and with even potential for model scalability. In this work, we systematically investigate why current VQ-based methods perform well and explore the limitations of existing diffusion-based methods from the perspective of motion data representation and distribution. Drawing on these insights, we preserve the inherent strengths of a diffusion-based human motion generation model and gradually optimize it with inspiration from VQ-based approaches. Our approach introduces a human motion diffusion model enabled to perform masked autoregression, optimized with a reformed data representation and distribution. Additionally, we propose a more robust evaluation method to assess different approaches. Extensive experiments on various datasets demonstrate our method outperforms previous methods and achieves state-of-the-art performances. | [
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219 | StyleMaster: Stylize Your Video with Artistic Generation and Translation | [
"Zixuan Ye",
"Huijuan Huang",
"Xintao Wang",
"Pengfei Wan",
"Di Zhang",
"Wenhan Luo"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ye_StyleMaster_Stylize_Your_Video_with_Artistic_Generation_and_Translation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_StyleMaster_Stylize_Your_Video_with_Artistic_Generation_and_Translation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_StyleMaster_Stylize_Your_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.07744 | @InProceedings{Ye_2025_CVPR,
author = {Ye, Zixuan and Huang, Huijuan and Wang, Xintao and Wan, Pengfei and Zhang, Di and Luo, Wenhan},
title = {StyleMaster: Stylize Your Video with Artistic Generation and Translation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2630-2640}
} | Style control has been popular in video generation models. Existing methods often generate videos far from the given style, cause content leakage, and struggle to transfer one video to the desired style. Our first observation is that the style extraction stage matters, whereas existing methods emphasize global style but ignore local textures. In order to bring texture features while preventing content leakage, we filter content-related patches while retaining style ones based on prompt-patch similarity; for global style extraction, we generate a paired style dataset through model illusion to facilitate contrastive learning, which greatly enhances the absolute style consistency. Moreover, to fill in the image-to-video gap, we train a lightweight motion adapter on still videos, which implicitly enhances stylization extent, and enables our image-trained model to be seamlessly applied to videos. Benefited from these efforts, our approach, StyleMaster, not only achieves significant improvement in both style resemblance and temporal coherence, but also can easily generalize to video style transfer with a gray tile ControlNet. Extensive experiments and visualizations demonstrate that StyleMaster significantly outperforms competitors, effectively generating high-quality stylized videos that align with textual content and closely resemble the style of reference images. | [
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220 | Unsupervised Continual Domain Shift Learning with Multi-Prototype Modeling | [
"Haopeng Sun",
"Yingwei Zhang",
"Lumin Xu",
"Sheng Jin",
"Ping Luo",
"Chen Qian",
"Wentao Liu",
"Yiqiang Chen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Unsupervised_Continual_Domain_Shift_Learning_with_Multi-Prototype_Modeling_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Unsupervised_Continual_Domain_Shift_Learning_with_Multi-Prototype_Modeling_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Unsupervised_Continual_Domain_CVPR_2025_supplemental.pdf | null | @InProceedings{Sun_2025_CVPR,
author = {Sun, Haopeng and Zhang, Yingwei and Xu, Lumin and Jin, Sheng and Luo, Ping and Qian, Chen and Liu, Wentao and Chen, Yiqiang},
title = {Unsupervised Continual Domain Shift Learning with Multi-Prototype Modeling},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10131-10141}
} | In real-world applications, deep neural networks may encounter constantly changing environments, where the test data originates from continually shifting unlabeled target domains. This problem, known as Unsupervised Continual Domain Shift Learning (UCDSL), poses practical difficulties. Existing methods for UCDSL aim to learn domain-invariant representations for all target domains. However, due to the existence of adaptivity gap, the invariant representation may theoretically lead to large joint errors. To overcome the limitation, we propose a novel UCDSL method, called Multi-Prototype Modeling (MPM). Our model comprises two key components: (1) Multi-Prototype Learning (MPL) for acquiring domain-specific representations using multiple domain-specific prototypes. MPL achieves domain-specific error minimization instead of enforcing feature alignment across different domains. (2) Bi-Level Graph Enhancer (BiGE) for enhancing domain-level and category-level representations, resulting in more accurate predictions. We provide theoretical and empirical analysis to demonstrate the effectiveness of our proposed method. We evaluate our approach on multiple benchmark datasets and show that our model surpasses state-of-the-art methods across all datasets, highlighting its effectiveness and robustness in handling unsupervised continual domain shift learning. Codes will be publicly accessible. | [
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221 | OmniGuard: Hybrid Manipulation Localization via Augmented Versatile Deep Image Watermarking | [
"Xuanyu Zhang",
"Zecheng Tang",
"Zhipei Xu",
"Runyi Li",
"Youmin Xu",
"Bin Chen",
"Feng Gao",
"Jian Zhang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_OmniGuard_Hybrid_Manipulation_Localization_via_Augmented_Versatile_Deep_Image_Watermarking_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_OmniGuard_Hybrid_Manipulation_Localization_via_Augmented_Versatile_Deep_Image_Watermarking_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_OmniGuard_Hybrid_Manipulation_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.01615 | @InProceedings{Zhang_2025_CVPR,
author = {Zhang, Xuanyu and Tang, Zecheng and Xu, Zhipei and Li, Runyi and Xu, Youmin and Chen, Bin and Gao, Feng and Zhang, Jian},
title = {OmniGuard: Hybrid Manipulation Localization via Augmented Versatile Deep Image Watermarking},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3008-3018}
} | With the rapid growth of generative AI and its widespread application in image editing, new risks have emerged regarding the authenticity and integrity of digital content. Existing versatile watermarking approaches suffer from trade-offs between tamper localization precision and visual quality. Constrained by the limited flexibility of previous framework, their localized watermark must remain fixed across all images. Under AIGC-editing, their copyright extraction accuracy is also unsatisfactory. To address these challenges, we propose OmniGuard, a novel augmented versatile watermarking approach that integrates proactive embedding with passive, blind extraction for robust copyright protection and tamper localization. OmniGuard employs a hybrid forensic framework that enables flexible localization watermark selection and introduces a degradation-aware tamper extraction network for precise localization under challenging conditions. Additionally, a lightweight AIGC-editing simulation layer is designed to enhance robustness across global and local editing. Extensive experiments show that OmniGuard achieves superior fidelity, robustness, and flexibility. Compared to the recent state-of-the-art approach EditGuard, our method outperforms it by 4.25dB in PSNR of the container image, 20.7% in F1-Score under noisy conditions, and 14.8% in average bit accuracy. | [
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] |
222 | Open-Canopy: Towards Very High Resolution Forest Monitoring | [
"Fajwel Fogel",
"Yohann Perron",
"Nikola Besic",
"Laurent Saint-André",
"Agnès Pellissier-Tanon",
"Martin Schwartz",
"Thomas Boudras",
"Ibrahim Fayad",
"Alexandre d'Aspremont",
"Loic Landrieu",
"Philippe Ciais"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Fogel_Open-Canopy_Towards_Very_High_Resolution_Forest_Monitoring_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Fogel_Open-Canopy_Towards_Very_High_Resolution_Forest_Monitoring_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fogel_Open-Canopy_Towards_Very_CVPR_2025_supplemental.pdf | null | @InProceedings{Fogel_2025_CVPR,
author = {Fogel, Fajwel and Perron, Yohann and Besic, Nikola and Saint-Andr\'e, Laurent and Pellissier-Tanon, Agn\`es and Schwartz, Martin and Boudras, Thomas and Fayad, Ibrahim and d'Aspremont, Alexandre and Landrieu, Loic and Ciais, Philippe},
title = {Open-Canopy: Towards Very High Resolution Forest Monitoring},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1395-1406}
} | Estimating canopy height and its changes at meter resolution from satellite imagery is a significant challenge in computer vision with critical environmental applications. However, the lack of open-access datasets at this resolution hinders the reproducibility and evaluation of models. We introduce Open-Canopy, the first open-access, country-scale benchmark for very high-resolution (1.5 m) canopy height estimation, covering over 87,000 km2 across France with 1.5 m resolution satellite imagery and aerial LiDAR data. Additionally, we present Open-Canopy-, a benchmark for canopy height change detection between images from different years at tree level--a challenging task for current computer vision models. We evaluate state-of-the-art architectures on these benchmarks, highlighting significant challenges and opportunities for improvement. Our datasets and code are publicly available at https://github.com/fajwel/Open-Canopy. | [
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] |
223 | ClearSight: Visual Signal Enhancement for Object Hallucination Mitigation in Multimodal Large Language Models | [
"Hao Yin",
"Guangzong Si",
"Zilei Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yin_ClearSight_Visual_Signal_Enhancement_for_Object_Hallucination_Mitigation_in_Multimodal_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_ClearSight_Visual_Signal_Enhancement_for_Object_Hallucination_Mitigation_in_Multimodal_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yin_ClearSight_Visual_Signal_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.13107 | @InProceedings{Yin_2025_CVPR,
author = {Yin, Hao and Si, Guangzong and Wang, Zilei},
title = {ClearSight: Visual Signal Enhancement for Object Hallucination Mitigation in Multimodal Large Language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14625-14634}
} | Contrastive decoding strategies are widely used to mitigate object hallucinations in multimodal large language models (MLLMs). By reducing over-reliance on language priors, these strategies ensure that generated content remains closely grounded in visual inputs, producing contextually accurate outputs. Since contrastive decoding requires no additional training or external tools, it offers both computational efficiency and versatility, making it highly attractive. However, these methods present two main limitations: (1) bluntly suppressing language priors can compromise coherence and accuracy of generated content, and (2) processing contrastive inputs adds computational load, significantly slowing inference speed. To address these challenges, we propose Visual Amplification Fusion (VAF), a plug-and-play technique that enhances attention to visual signals within the model's middle layers, where modality fusion predominantly occurs. This approach enables more effective capture of visual features, reducing the model's bias toward language modality. Experimental results demonstrate that VAF significantly reduces hallucinations across various MLLMs without affecting inference speed, while maintaining coherence and accuracy in generated outputs. | [
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] |
224 | Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget | [
"Vikash Sehwag",
"Xianghao Kong",
"Jingtao Li",
"Michael Spranger",
"Lingjuan Lyu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Sehwag_Stretching_Each_Dollar_Diffusion_Training_from_Scratch_on_a_Micro-Budget_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Sehwag_Stretching_Each_Dollar_Diffusion_Training_from_Scratch_on_a_Micro-Budget_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sehwag_Stretching_Each_Dollar_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2407.15811 | @InProceedings{Sehwag_2025_CVPR,
author = {Sehwag, Vikash and Kong, Xianghao and Li, Jingtao and Spranger, Michael and Lyu, Lingjuan},
title = {Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28596-28608}
} | As scaling laws in generative AI push performance, they simultaneously concentrate the development of these models among actors with large computational resources. With a focus on text-to-image (T2I) generative models, we aim to unlock this bottleneck by demonstrating very low-cost training of large-scale T2I diffusion transformer models. As the computational cost of transformers increases with the number of patches in each image, we propose randomly masking up to 75% of the image patches during training. We propose a deferred masking strategy that preprocesses all patches using a patch-mixer before masking, thus significantly reducing the performance degradation with masking, making it superior to model downscaling in reducing computational cost. We also incorporate the latest improvements in transformer architecture, such as the use of mixture-of-experts layers, to improve performance and further identify the critical benefit of using synthetic images in micro-budget training. Finally, using only 37M publicly available real and synthetic images, we train a 1.16 billion parameter sparse transformer with only 1,890 USD economical cost and achieve a 12.7 FID in zero-shot generation on the COCO dataset. Notably, our model achieves competitive performance across both automated and human-centric evaluations, as well as high-quality generations, while incurring 118xlower costs than Stable Diffusion models and 14xlower costs than the current state-of-the-art approach, which costs \28,400. We also further investigate the influence of synthetic images on performance and demonstrate that micro-budget training on only synthetic images is sufficient for achieving high-quality data generation. Our end-to-end training pipeline and model checkpoints are available at https://github.com/SonyResearch/micro_diffusion. | [
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] |
225 | Guiding Human-Object Interactions with Rich Geometry and Relations | [
"Mengqing Xue",
"Yifei Liu",
"Ling Guo",
"Shaoli Huang",
"Changxing Ding"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Xue_Guiding_Human-Object_Interactions_with_Rich_Geometry_and_Relations_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Xue_Guiding_Human-Object_Interactions_with_Rich_Geometry_and_Relations_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xue_Guiding_Human-Object_Interactions_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.20172 | @InProceedings{Xue_2025_CVPR,
author = {Xue, Mengqing and Liu, Yifei and Guo, Ling and Huang, Shaoli and Ding, Changxing},
title = {Guiding Human-Object Interactions with Rich Geometry and Relations},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22714-22723}
} | Human-object interaction (HOI) synthesis is crucial for creating immersive and realistic experiences for applications such as virtual reality. Existing methods often rely on simplified object representations, such as the object's centroid or the nearest point to a human, to achieve physically plausible motions. However, these approaches may overlook geometric complexity, resulting in suboptimal interaction fidelity. To address this limitation, we introduce ROG, a novel diffusion-based framework that models the spatiotemporal relationships inherent in HOIs with rich geometric detail. For efficient object representation, we select boundary-focused and fine-detail key points from the object mesh, ensuring a comprehensive depiction of the object's geometry. This representation is used to construct an interactive distance field (IDF), capturing the robust HOI dynamics. Furthermore, we develop a diffusion-based relation model that integrates spatial and temporal attention mechanisms, enabling a better understanding of intricate HOI relationships. This relation model refines the generated motion's IDF, guiding the motion generation process to produce relation-aware and semantically aligned movements. Experimental evaluations demonstrate that ROG significantly outperforms state-of-the-art methods in the realism and semantic accuracy of synthesized HOIs. This paper's code will be released. | [
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226 | TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion | [
"Yiran Wang",
"Jiaqi Li",
"Chaoyi Hong",
"Ruibo Li",
"Liusheng Sun",
"Xiao Song",
"Zhe Wang",
"Zhiguo Cao",
"Guosheng Lin"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_TacoDepth_Towards_Efficient_Radar-Camera_Depth_Estimation_with_One-stage_Fusion_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_TacoDepth_Towards_Efficient_Radar-Camera_Depth_Estimation_with_One-stage_Fusion_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_TacoDepth_Towards_Efficient_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2504.11773 | @InProceedings{Wang_2025_CVPR,
author = {Wang, Yiran and Li, Jiaqi and Hong, Chaoyi and Li, Ruibo and Sun, Liusheng and Song, Xiao and Wang, Zhe and Cao, Zhiguo and Lin, Guosheng},
title = {TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10523-10533}
} | Radar-Camera depth estimation aims to predict dense and accurate metric depth by fusing input images and Radar data. Model efficiency is crucial for this task in pursuit of real-time processing on autonomous vehicles and robotic platforms. However, due to the sparsity of Radar returns, the prevailing methods adopt multi-stage frameworks with intermediate quasi-dense depth, which are time-consuming and not robust. To address these challenges, we propose TacoDepth, an efficient and accurate Radar-Camera depth estimation model with one-stage fusion. Specifically, the graph-based Radar structure extractor and the pyramid-based Radar fusion module are designed to capture and integrate the graph structures of Radar point clouds, delivering superior model efficiency and robustness without relying on the intermediate depth results. Moreover, TacoDepth can be flexible for different inference modes, providing a better balance of speed and accuracy. Extensive experiments are conducted to demonstrate the efficacy of our method. Compared with the previous state-of-the-art approach, TacoDepth improves depth accuracy and processing speed by 12.8% and 91.8%. Our work provides a new perspective on efficient Radar-Camera depth estimation. | [
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] |
227 | Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment | [
"Hiromu Taketsugu",
"Takeru Oba",
"Takahiro Maeda",
"Shohei Nobuhara",
"Norimichi Ukita"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Taketsugu_Physical_Plausibility-aware_Trajectory_Prediction_via_Locomotion_Embodiment_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Taketsugu_Physical_Plausibility-aware_Trajectory_Prediction_via_Locomotion_Embodiment_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Taketsugu_Physical_Plausibility-aware_Trajectory_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.17267 | @InProceedings{Taketsugu_2025_CVPR,
author = {Taketsugu, Hiromu and Oba, Takeru and Maeda, Takahiro and Nobuhara, Shohei and Ukita, Norimichi},
title = {Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12324-12334}
} | Humans can predict future human trajectories even from momentary observations by using human pose-related cues. However, previous Human Trajectory Prediction (HTP) methods leverage the pose cues implicitly, resulting in implausible predictions. To address this, we propose Locomotion Embodiment, a framework that explicitly evaluates the physical plausibility of the predicted trajectory by locomotion generation under the laws of physics. While the plausibility of locomotion is learned with an indifferentiable physics simulator, it is replaced by our differentiable Locomotion Value function to train an HTP network in a data-driven manner. In particular, our proposed Embodied Locomotion loss is beneficial for efficiently training a stochastic HTP network using multiple heads. Furthermore, the Locomotion Value filter is proposed to filter out implausible trajectories at inference. Experiments demonstrate that our method enhances even the state-of-the-art HTP methods across diverse datasets and problem settings. Our code is available at: https://github.com/ImIntheMiddle/EmLoco. | [
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228 | CADDreamer: CAD Object Generation from Single-view Images | [
"Yuan Li",
"Cheng Lin",
"Yuan Liu",
"Xiaoxiao Long",
"Chenxu Zhang",
"Ningna Wang",
"Xin Li",
"Wenping Wang",
"Xiaohu Guo"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Li_CADDreamer_CAD_Object_Generation_from_Single-view_Images_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Li_CADDreamer_CAD_Object_Generation_from_Single-view_Images_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_CADDreamer_CAD_Object_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2502.20732 | @InProceedings{Li_2025_CVPR,
author = {Li, Yuan and Lin, Cheng and Liu, Yuan and Long, Xiaoxiao and Zhang, Chenxu and Wang, Ningna and Li, Xin and Wang, Wenping and Guo, Xiaohu},
title = {CADDreamer: CAD Object Generation from Single-view Images},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21448-21457}
} | The field of diffusion-based 3D generation has experienced tremendous progress in recent times. However, existing 3D generative models often produce overly dense and unstructured meshes, which are in stark contrast to the compact, structured and clear-edged CAD models created by human modelers. We introduce CADDreamer, a novel method for generating CAD objects from a single image. This method proposes a primitive-aware multi-view diffusion model, which perceives both local geometry and high-level structural semantics during the generation process. We encode primitive semantics into the color domain, and enforce the strong priors in pre-trained diffusion models to align with the well-defined primitives. As a result, we can infer multi-view normal maps and semantic maps from a single image, thereby reconstructing a mesh with primitive labels. Correspondingly, we propose a set of fitting and optimization methods to deal with the inevitable noise and distortion in generated primitives, ultimately producing a complete and seamless Boundary Representation (B-rep) of a Computer-Aided Design (CAD) model. Experimental results demonstrate that our method can effectively recover high-quality CAD objects from single-view images. Compared to existing 3D generation methods, the models produced by CADDreamer are compact in representation, clear in structure, sharp in boundaries, and watertight in topology. | [
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] |
229 | Vision-Language Model IP Protection via Prompt-based Learning | [
"Lianyu Wang",
"Meng Wang",
"Huazhu Fu",
"Daoqiang Zhang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Vision-Language_Model_IP_Protection_via_Prompt-based_Learning_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Vision-Language_Model_IP_Protection_via_Prompt-based_Learning_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Vision-Language_Model_IP_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.02393 | @InProceedings{Wang_2025_CVPR,
author = {Wang, Lianyu and Wang, Meng and Fu, Huazhu and Zhang, Daoqiang},
title = {Vision-Language Model IP Protection via Prompt-based Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9497-9506}
} | Vision-language models (VLMs) like CLIP (Contrastive Language-Image Pre-Training) have seen remarkable success in visual recognition, highlighting the increasing need to safeguard the intellectual property (IP) of well-trained models. Effective IP protection extends beyond ensuring authorized usage; it also necessitates restricting model deployment to authorized data domains, particularly when the model is fine-tuned for specific target domains. However, current IP protection methods often rely solely on the visual backbone, which may lack sufficient semantic richness. To bridge this gap, we introduce IP-CLIP, a lightweight IP protection strategy tailored to CLIP, employing a prompt-based learning approach. By leveraging the frozen visual backbone of CLIP, we extract both image style and content information, incorporating them into the learning of IP prompt. This strategy acts as a robust barrier, effectively preventing the unauthorized transfer of features from authorized domains to unauthorized ones. Additionally, we propose a style-enhancement branch that constructs feature banks for both authorized and unauthorized domains. This branch integrates self-enhanced and cross-domain features, further strengthening IP-CLIP's capability to block features from unauthorized domains. Finally, we present new three metrics designed to better balance the performance degradation of authorized and unauthorized domains. Comprehensive experiments in various scenarios demonstrate its promising potential for application in IP protection tasks for VLMs. | [
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230 | Where's the Liability in the Generative Era? Recovery-based Black-Box Detection of AI-Generated Content | [
"Haoyue Bai",
"Yiyou Sun",
"Wei Cheng",
"Haifeng Chen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Bai_Wheres_the_Liability_in_the_Generative_Era_Recovery-based_Black-Box_Detection_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Bai_Wheres_the_Liability_in_the_Generative_Era_Recovery-based_Black-Box_Detection_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bai_Wheres_the_Liability_CVPR_2025_supplemental.pdf | null | @InProceedings{Bai_2025_CVPR,
author = {Bai, Haoyue and Sun, Yiyou and Cheng, Wei and Chen, Haifeng},
title = {Where's the Liability in the Generative Era? Recovery-based Black-Box Detection of AI-Generated Content},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28821-28830}
} | The recent proliferation of photorealistic images created by generative models has sparked both excitement and concern, as these images are increasingly indistinguishable from real ones to the human eye. While offering new creative and commercial possibilities, the potential for misuse, such as in misinformation and fraud, highlights the need for effective detection methods. Current detection approaches often rely on access to model weights or require extensive collections of real image datasets, limiting their scalability and practical application in real-world scenarios. In this work, we introduce a novel black-box detection framework that requires only API access, sidestepping the need for model weights or large auxiliary datasets. Our approach leverages a corrupt-and-recover strategy: by masking part of an image and assessing the model's ability to reconstruct it, we measure the likelihood that the image was generated by the model itself. For black-box models that do not support masked-image inputs, we incorporate a cost-efficient surrogate model trained to align with the target model's distribution, enhancing detection capability. Our framework demonstrates strong performance, outperforming baseline methods by 4.31% in mean average precision across eight diffusion model variant datasets. | [
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231 | Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation | [
"Jiantao Lin",
"Xin Yang",
"Meixi Chen",
"Yingjie Xu",
"Dongyu Yan",
"Leyi Wu",
"Xinli Xu",
"Lie Xu",
"Shunsi Zhang",
"Ying-Cong Chen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lin_Kiss3DGen_Repurposing_Image_Diffusion_Models_for_3D_Asset_Generation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_Kiss3DGen_Repurposing_Image_Diffusion_Models_for_3D_Asset_Generation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_Kiss3DGen_Repurposing_Image_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.01370 | @InProceedings{Lin_2025_CVPR,
author = {Lin, Jiantao and Yang, Xin and Chen, Meixi and Xu, Yingjie and Yan, Dongyu and Wu, Leyi and Xu, Xinli and Xu, Lie and Zhang, Shunsi and Chen, Ying-Cong},
title = {Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {5870-5880}
} | Diffusion models have achieved great success in generating 2D images. However, the quality and generalizability of 3D content generation remain limited. State-of-the-art methods often require large-scale 3D assets for training, which are challenging to collect. In this work, we introduce Kiss3DGen (Keep It Simple and Straightforward in 3D Generation), an efficient framework for generating, editing, and enhancing 3D objects by repurposing a well-trained 2D image diffusion model for 3D generation. Specifically, we fine-tune a diffusion model to generate "3D Bundle Image", a tiled representation composed of multi-view images and their corresponding normal maps. The normal maps are then used to reconstruct a 3D mesh, and the multi-view images provide texture mapping, resulting in a complete 3D model. This simple method effectively transforms the 3D generation problem into a 2D image generation task, maximizing the utilization of knowledge in pretrained diffusion models. Furthermore, we demonstrate that our Kiss3DGen model is compatible with various diffusion model techniques, enabling advanced features such as 3D editing, mesh and texture enhancement, etc. Through extensive experiments, we demonstrate the effectiveness of our approach, showcasing its ability to produce high-quality 3D models efficiently. | [
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232 | DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations | [
"Krishna Sri Ipsit Mantri",
"Carola-Bibiane Schönlieb",
"Bruno Ribeiro",
"Chaim Baskin",
"Moshe Eliasof"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Mantri_DiTASK_Multi-Task_Fine-Tuning_with_Diffeomorphic_Transformations_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Mantri_DiTASK_Multi-Task_Fine-Tuning_with_Diffeomorphic_Transformations_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mantri_DiTASK_Multi-Task_Fine-Tuning_CVPR_2025_supplemental.pdf | null | @InProceedings{Mantri_2025_CVPR,
author = {Mantri, Krishna Sri Ipsit and Sch\"onlieb, Carola-Bibiane and Ribeiro, Bruno and Baskin, Chaim and Eliasof, Moshe},
title = {DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25218-25229}
} | Pre-trained Vision Transformers now serve as powerful tools for computer vision. Yet, efficiently adapting them for multiple tasks remains a challenge that arises from the need to modify the rich hidden representations encoded by the learned weight matrices, without inducing interference between tasks. Current parameter-efficient methods like LoRA, which apply low-rank updates, force tasks to compete within constrained subspaces, ultimately degrading performance. We introduce DiTASK, a novel Diffeomorphic Multi-Task Fine-Tuning approach that maintains pre-trained representations by preserving weight matrix singular vectors, while enabling task-specific adaptations through neural diffeomorphic transformations of the singular values. By following this approach, DiTASK enables both shared and task-specific feature modulations with minimal added parameters. Our theoretical analysis shows that DiTASK achieves full-rank updates during optimization, preserving the geometric structure of pre-trained features, and establishing a new paradigm for efficient multi-task learning (MTL). Our experiments on PASCAL MTL and NYUD show that DiTASK achieves state-of-the-art performance across four dense prediction tasks, using 75% fewer parameters than existing methods. | [
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] |
233 | OW-OVD: Unified Open World and Open Vocabulary Object Detection | [
"Xing Xi",
"Yangyang Huang",
"Ronghua Luo",
"Yu Qiu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Xi_OW-OVD_Unified_Open_World_and_Open_Vocabulary_Object_Detection_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Xi_OW-OVD_Unified_Open_World_and_Open_Vocabulary_Object_Detection_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xi_OW-OVD_Unified_Open_CVPR_2025_supplemental.pdf | null | @InProceedings{Xi_2025_CVPR,
author = {Xi, Xing and Huang, Yangyang and Luo, Ronghua and Qiu, Yu},
title = {OW-OVD: Unified Open World and Open Vocabulary Object Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25454-25464}
} | Open world perception expands traditional closed-set frameworks, which assume a predefined set of known categories, to encompass dynamic real-world environments. Open World Object Detection (OWOD) and Open Vocabulary Object Detection (OVD) are two main research directions, each addressing unique challenges in dynamic environments. However, existing studies often focus on only one of these tasks, leaving the combined challenges of OWOD and OVD largely underexplored. In this paper, we propose a novel detector, OW-OVD, which inherits the zero-shot generalization capability of OVD detectors while incorporating the ability to actively detect unknown objects and progressively optimize performance through incremental learning, as seen in OWOD detectors. To achieve this, we start with a standard OVD detector and adapt it for OWOD tasks. For attribute selection, we propose the Visual Similarity Attribute Selection (VSAS) method, which identifies the most generalizable attributes by computing similarity distributions across annotated and unannotated regions. Additionally, to ensure the diversity of attributes, we incorporate a similarity constraint in the iterative process. Finally, to preserve the standard inference process of OVD, we propose the Hybrid Attribute-Uncertainty Fusion (HAUF) method. This method combines attribute similarity with known class uncertainty to infer the likelihood of an object belonging to an unknown class. We validated the effectiveness of OW-OVD through evaluations on two OWOD benchmarks, M-OWODB and S-OWODB. The results demonstrate that OW-OVD outperforms existing state-of-the-art models, achieving a +15.3 improvement in unknown object recall (U-Recall) and a +15.5 increase in unknown class average precision (U-mAP). Our code is available at: https://github.com/xxyzll/OW_OVD. | [
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] |
234 | Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing | [
"Bingliang Zhang",
"Wenda Chu",
"Julius Berner",
"Chenlin Meng",
"Anima Anandkumar",
"Yang Song"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Improving_Diffusion_Inverse_Problem_Solving_with_Decoupled_Noise_Annealing_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Improving_Diffusion_Inverse_Problem_Solving_with_Decoupled_Noise_Annealing_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Improving_Diffusion_Inverse_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2407.01521 | @InProceedings{Zhang_2025_CVPR,
author = {Zhang, Bingliang and Chu, Wenda and Berner, Julius and Meng, Chenlin and Anandkumar, Anima and Song, Yang},
title = {Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20895-20905}
} | Diffusion models have recently achieved success in solving Bayesian inverse problems with learned data priors. Current methods build on top of the diffusion sampling process, where each denoising step makes small modifications to samples from the previous step. However, this process struggles to correct errors from earlier sampling steps, leading to worse performance in complicated nonlinear inverse problems, such as phase retrieval. To address this challenge, we propose a new method called Decoupled Annealing Posterior Sampling (DAPS) that relies on a novel noise annealing process. Specifically, we decouple consecutive steps in a diffusion sampling trajectory, allowing them to vary considerably from one another while ensuring their time-marginals anneal to the true posterior as we reduce noise levels. This approach enables the exploration of a larger solution space, improving the success rate for accurate reconstructions. We demonstrate that DAPS significantly improves sample quality and stability across multiple image restoration tasks, particularly in complicated nonlinear inverse problems. | [
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235 | AvatarArtist: Open-Domain 4D Avatarization | [
"Hongyu Liu",
"Xuan Wang",
"Ziyu Wan",
"Yue Ma",
"Jingye Chen",
"Yanbo Fan",
"Yujun Shen",
"Yibing Song",
"Qifeng Chen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_AvatarArtist_Open-Domain_4D_Avatarization_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_AvatarArtist_Open-Domain_4D_Avatarization_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_AvatarArtist_Open-Domain_4D_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.19906 | @InProceedings{Liu_2025_CVPR,
author = {Liu, Hongyu and Wang, Xuan and Wan, Ziyu and Ma, Yue and Chen, Jingye and Fan, Yanbo and Shen, Yujun and Song, Yibing and Chen, Qifeng},
title = {AvatarArtist: Open-Domain 4D Avatarization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10758-10769}
} | This work focuses on open-domain 4D avatarization, with the purpose of creating a 4D avatar from a portrait image in an arbitrary style. We select parametric triplanes as the intermediate 4D representation, and propose a practical training paradigm that takes advantage of both generative adversarial networks (GANs) and diffusion models. Our design stems from the observation that 4D GANs excel at bridging images and triplanes without supervision yet usually face challenges in handling diverse data distributions. A robust 2D diffusion prior emerges as the solution, assisting the GAN in transferring its expertise across various domains. The synergy between these experts permits the construction of a multi-domain image-triplane dataset, which drives the development of a general 4D avatar creator. Extensive experiments suggest that our model, termed AvatarArtist, is capable of producing high-quality 4D avatars with strong robustness to various source image domains. The code, the data, and the models will be made publicly available to facilitate future studies. | [
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236 | DesignDiffusion: High-Quality Text-to-Design Image Generation with Diffusion Models | [
"Zhendong Wang",
"Jianmin Bao",
"Shuyang Gu",
"Dong Chen",
"Wengang Zhou",
"Houqiang Li"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_DesignDiffusion_High-Quality_Text-to-Design_Image_Generation_with_Diffusion_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_DesignDiffusion_High-Quality_Text-to-Design_Image_Generation_with_Diffusion_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_DesignDiffusion_High-Quality_Text-to-Design_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.01645 | @InProceedings{Wang_2025_CVPR,
author = {Wang, Zhendong and Bao, Jianmin and Gu, Shuyang and Chen, Dong and Zhou, Wengang and Li, Houqiang},
title = {DesignDiffusion: High-Quality Text-to-Design Image Generation with Diffusion Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20906-20915}
} | In this paper, we present DesignDiffusion, a simple yet effective framework for the novel task of synthesizing design images from textual descriptions. A primary challenge lies in generating accurate and style-consistent textual and visual content. Existing works in a related task of visual text generation often focus on generating text within given specific regions, which limits the creativity of generation models, resulting in style or color inconsistencies between textual and visual elements if applied to design image generation. To address this issue, we propose an end-to-end, one-stage diffusion-based framework that avoids intricate components like position and layout modeling. Specifically, the proposed framework directly synthesizes textual and visual design elements from user prompts. It utilizes a distinctive character embedding derived from the visual text to enhance the input prompt, along with a character localization loss for enhanced supervision during text generation. Furthermore, we employ a self-play Direct Preference Optimization fine-tuning strategy to improve the quality and accuracy of the synthesized visual text. Extensive experiments demonstrate that DesignDiffusion achieves state-of-the-art performance in design image generation. | [
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] |
237 | Using Powerful Prior Knowledge of Diffusion Model in Deep Unfolding Networks for Image Compressive Sensing | [
"Chen Liao",
"Yan Shen",
"Dan Li",
"Zhongli Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liao_Using_Powerful_Prior_Knowledge_of_Diffusion_Model_in_Deep_Unfolding_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_Using_Powerful_Prior_Knowledge_of_Diffusion_Model_in_Deep_Unfolding_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liao_Using_Powerful_Prior_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.08429 | @InProceedings{Liao_2025_CVPR,
author = {Liao, Chen and Shen, Yan and Li, Dan and Wang, Zhongli},
title = {Using Powerful Prior Knowledge of Diffusion Model in Deep Unfolding Networks for Image Compressive Sensing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18000-18010}
} | Recently, Deep Unfolding Networks (DUNs) have achieved impressive reconstruction quality in the field of image Compressive Sensing (CS) by unfolding iterative optimization algorithms into neural networks. The reconstruction quality of DUNs depends on the learned prior knowledge, so introducing stronger prior knowledge can further improve reconstruction quality. On the other hand, pre-trained diffusion models contain powerful prior knowledge and have a solid theoretical foundation and strong scalability, but it requires a large number of iterative steps to achieve reconstruction. In this paper, we propose to use the powerful prior knowledge of pre-trained diffusion model in DUNs to achieve high-quality reconstruction with less steps for image CS. Specifically, we first design an iterative optimization algorithm named Diffusion Message Passing (DMP), which embeds a pre-trained diffusion model into each iteration process of DMP. Then, we deeply unfold the DMP algorithm into a neural network named DMP-DUN. The proposed DMP-DUN can use lightweight neural networks to achieve mapping from measurement data to the intermediate steps of the reverse diffusion process and directly approximate the divergence of the diffusion model, thereby further improving reconstruction efficiency. Extensive experiments show that our proposed DMP-DUN achieves state-of-the-art performance and requires at least only 2 steps to reconstruct the image. Codes are available at https://github.com/FengodChen/DMP-DUN-CVPR2025. | [
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238 | Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Content | [
"Qiuheng Wang",
"Yukai Shi",
"Jiarong Ou",
"Rui Chen",
"Ke Lin",
"Jiahao Wang",
"Boyuan Jiang",
"Haotian Yang",
"Mingwu Zheng",
"Xin Tao",
"Fei Yang",
"Pengfei Wan",
"Di Zhang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Koala-36M_A_Large-scale_Video_Dataset_Improving_Consistency_between_Fine-grained_Conditions_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Koala-36M_A_Large-scale_Video_Dataset_Improving_Consistency_between_Fine-grained_Conditions_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Koala-36M_A_Large-scale_CVPR_2025_supplemental.pdf | null | @InProceedings{Wang_2025_CVPR,
author = {Wang, Qiuheng and Shi, Yukai and Ou, Jiarong and Chen, Rui and Lin, Ke and Wang, Jiahao and Jiang, Boyuan and Yang, Haotian and Zheng, Mingwu and Tao, Xin and Yang, Fei and Wan, Pengfei and Zhang, Di},
title = {Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Content},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8428-8437}
} | With the continuous progress of visual generation technologies, the scale of video datasets has grown exponentially. The quality of these datasets plays a pivotal role in the performance of video generation models. We assert that temporal splitting, detailed captions, and video quality filtering are three crucial determinants of dataset quality. However, existing datasets exhibit various limitations in these areas. To address these challenges, we introduce Koala-36M, a large-scale, high-quality video dataset featuring accurate temporal splitting, detailed captions, and superior video quality. The essence of our approach lies in improving the consistency between fine-grained conditions and video content. Specifically, we employ a linear classifier on probability distributions to enhance the accuracy of transition detection, ensuring better temporal consistency. We then provide structured captions for the splitted videos, with an average length of 200 words, to improve text-video alignment. Additionally, we develop a Video Training Suitability Score (VTSS) that integrates multiple sub-metrics, allowing us to filter high-quality videos from the original corpus. Finally, we incorporate several metrics into the training process of the generation model, further refining the fine-grained conditions. Our experiments demonstrate the effectiveness of our data processing pipeline and the quality of the proposed Koala-36M dataset. Our dataset and code have been released at https://koala36m.github.io/. | [
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239 | VASparse: Towards Efficient Visual Hallucination Mitigation via Visual-Aware Token Sparsification | [
"Xianwei Zhuang",
"Zhihong Zhu",
"Yuxin Xie",
"Liming Liang",
"Yuexian Zou"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhuang_VASparse_Towards_Efficient_Visual_Hallucination_Mitigation_via_Visual-Aware_Token_Sparsification_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhuang_VASparse_Towards_Efficient_Visual_Hallucination_Mitigation_via_Visual-Aware_Token_Sparsification_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhuang_VASparse_Towards_Efficient_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2501.06553 | @InProceedings{Zhuang_2025_CVPR,
author = {Zhuang, Xianwei and Zhu, Zhihong and Xie, Yuxin and Liang, Liming and Zou, Yuexian},
title = {VASparse: Towards Efficient Visual Hallucination Mitigation via Visual-Aware Token Sparsification},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4189-4199}
} | Large Vision-Language Models (LVLMs) may produce outputs that are unfaithful to reality, also known as visual hallucinations (VH), which significantly impedes their real-world usage. To alleviate VH, various decoding strategies have been proposed to enhance visual information. However, many of these methods may require secondary decoding and rollback, which significantly reduces inference speed. In this work, we propose an efficient plug-and-play decoding algorithm via Visual-Aware Sparsification (VASparse) from the perspective of token sparsity for mitigating VH. VASparse is inspired by empirical observations: (1) the sparse activation of attention in LVLMs, and (2) visual-agnostic tokens sparsification exacerbates VH. Based on these insights, we propose a novel token sparsification strategy that balances efficiency and trustworthiness. Specifically, VASparse implements a visual-aware token selection strategy during decoding to reduce redundant tokens while preserving visual context effectively. Additionally, we innovatively introduce a sparse-based visual contrastive decoding method to recalibrate the distribution of hallucinated outputs without the time overhead associated with secondary decoding. Subsequently, VASparse recalibrates attention scores to penalize attention sinking of LVLMs towards text tokens. Extensive experiments across four popular benchmarks confirm the effectiveness of VASparse in mitigating VH across different LVLM families without requiring additional training or post-processing. Impressively, VASparse achieves state-of-the-art performance for mitigating VH while maintaining competitive decoding speed. Code is available at https://github.com/mengchuang123/VASparse-github. | [
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240 | SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models | [
"Kevin Miller",
"Aditya Gangrade",
"Samarth Mishra",
"Kate Saenko",
"Venkatesh Saligrama"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Miller_SPARC_Score_Prompting_and_Adaptive_Fusion_for_Zero-Shot_Multi-Label_Recognition_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Miller_SPARC_Score_Prompting_and_Adaptive_Fusion_for_Zero-Shot_Multi-Label_Recognition_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Miller_SPARC_Score_Prompting_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2502.16911 | @InProceedings{Miller_2025_CVPR,
author = {Miller, Kevin and Gangrade, Aditya and Mishra, Samarth and Saenko, Kate and Saligrama, Venkatesh},
title = {SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4313-4321}
} | Zero-shot multi-label recognition (MLR) with Vision-Language Models (VLMs) faces significant challenges without training data, model tuning, or architectural modifications. Existing approaches require prompt tuning or architectural adaptations, limiting zero-shot applicability. Our work proposes a novel solution treating VLMs as black boxes, leveraging scores without training data or ground truth. We make two contributions. First, we find that VLM scores suffer from image- and prompt-specific biases, and that simple standardization is surprisingly effective at removing these and boosting MLR performance. And second, we introduce compound prompts grounded in realistic object combinations. Our analysis reveals "AND"/"OR" signal ambiguities that cause maximum compound scores to be surprisingly suboptimal compared to second-highest scores. We introduce an adaptive fusion method to address this issue. Our method enhances other zero-shot approaches, consistently improving their results. Experiments show superior mean Average Precision (mAP) compared to methods requiring training data, achieved through refined object ranking for robust zero-shot MLR. Code can be found at https://github.com/kjmillerCURIS/SPARC. | [
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] |
241 | UniGoal: Towards Universal Zero-shot Goal-oriented Navigation | [
"Hang Yin",
"Xiuwei Xu",
"Linqing Zhao",
"Ziwei Wang",
"Jie Zhou",
"Jiwen Lu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yin_UniGoal_Towards_Universal_Zero-shot_Goal-oriented_Navigation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_UniGoal_Towards_Universal_Zero-shot_Goal-oriented_Navigation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yin_UniGoal_Towards_Universal_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.10630 | @InProceedings{Yin_2025_CVPR,
author = {Yin, Hang and Xu, Xiuwei and Zhao, Linqing and Wang, Ziwei and Zhou, Jie and Lu, Jiwen},
title = {UniGoal: Towards Universal Zero-shot Goal-oriented Navigation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19057-19066}
} | In this paper, we propose a general framework for universal zero-shot goal-oriented navigation. Existing zero-shot methods build inference framework upon large language models (LLM) for specific tasks, which differs a lot in overall pipeline and fails to generalize across different types of goal. Towards the aim of universal zero-shot navigation, we propose a uniform graph representation to unify different goals, including object category, instance image and text description. We also convert the observation of agent into an online maintained scene graph. With this consistent scene and goal representation, we preserve most structural information compared with pure text and are able to leverage LLM for explicit graph-based reasoning.Specifically, we conduct graph matching between the scene graph and goal graph at each time instant and propose different strategies to generate long-term goal of exploration according to different matching states. The agent first iteratively searches subgraph of goal when zero-matched. With partial matching, the agent then utilizes coordinate projection and anchor pair alignment to infer the goal location. Finally scene graph correction and goal verification are applied for perfect matching. We also present a blacklist mechanism to enable robust switch between stages.Extensive experiments on several benchmarks show that our UniGoal achieves state-of-the-art zero-shot performance on three studied navigation tasks with a single model, even outperforming task-specific zero-shot methods and supervised universal methods. | [
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242 | Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation | [
"Kunpeng Qiu",
"Zhiqiang Gao",
"Zhiying Zhou",
"Mingjie Sun",
"Yongxin Guo"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Qiu_Noise-Consistent_Siamese-Diffusion_for_Medical_Image_Synthesis_and_Segmentation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Qiu_Noise-Consistent_Siamese-Diffusion_for_Medical_Image_Synthesis_and_Segmentation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qiu_Noise-Consistent_Siamese-Diffusion_for_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2505.06068 | @InProceedings{Qiu_2025_CVPR,
author = {Qiu, Kunpeng and Gao, Zhiqiang and Zhou, Zhiying and Sun, Mingjie and Guo, Yongxin},
title = {Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15672-15681}
} | Deep learning has revolutionized medical image segmentation, yet its full potential remains constrained by the paucity of annotated datasets. While diffusion models have emerged as a promising approach for generating synthetic image-mask pairs to augment these datasets, they paradoxically suffer from the same data scarcity challenges they aim to mitigate. Traditional mask-only models frequently yield low-fidelity images due to their inability to adequately capture morphological intricacies, which can critically compromise the robustness and reliability of segmentation models. To alleviate this limitation, we introduce Siamese-Diffusion, a novel dual-component model comprising Mask-Diffusion and Image-Diffusion. During training, a Noise Consistency Loss is introduced between these components to enhance the morphological fidelity of Mask-Diffusion in the parameter space. During sampling, only Mask-Diffusion is used, ensuring diversity and scalability. Comprehensive experiments demonstrate the superiority of our method. Siamese-Diffusion boosts SANet's mDice and mIoU by 3.6% and 4.4% on the Polyps, while UNet improves by 1.52% and 1.64% on the ISIC2018. | [
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] |
243 | DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection | [
"Jaewoo Song",
"Daemin Park",
"Kanghyun Baek",
"Sangyub Lee",
"Jooyoung Choi",
"Eunji Kim",
"Sungroh Yoon"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Song_DefectFill_Realistic_Defect_Generation_with_Inpainting_Diffusion_Model_for_Visual_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Song_DefectFill_Realistic_Defect_Generation_with_Inpainting_Diffusion_Model_for_Visual_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_DefectFill_Realistic_Defect_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.13985 | @InProceedings{Song_2025_CVPR,
author = {Song, Jaewoo and Park, Daemin and Baek, Kanghyun and Lee, Sangyub and Choi, Jooyoung and Kim, Eunji and Yoon, Sungroh},
title = {DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18718-18727}
} | Developing effective visual inspection models remains challenging due to the scarcity of defect data. While image generation models have been used to synthesize defect images, producing highly realistic defects remains difficult. We propose DefectFill, a novel method for realistic defect generation that requires only a few reference defect images. It leverages a fine-tuned inpainting diffusion model, optimized with our custom loss functions incorporating defect, object, and attention terms. It enables precise capture of detailed, localized defect features and their seamless integration into defect-free objects. Additionally, our Low-Fidelity Selection method further enhances the defect sample quality. Experiments show that DefectFill generates high-quality defect images, enabling visual inspection models to achieve state-of-the-art performance on the MVTec AD dataset. | [
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] |
244 | Less is More: Efficient Image Vectorization with Adaptive Parameterization | [
"Kaibo Zhao",
"Liang Bao",
"Yufei Li",
"Xu Su",
"Ke Zhang",
"Xiaotian Qiao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Less_is_More_Efficient_Image_Vectorization_with_Adaptive_Parameterization_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Less_is_More_Efficient_Image_Vectorization_with_Adaptive_Parameterization_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Less_is_More_CVPR_2025_supplemental.pdf | null | @InProceedings{Zhao_2025_CVPR,
author = {Zhao, Kaibo and Bao, Liang and Li, Yufei and Su, Xu and Zhang, Ke and Qiao, Xiaotian},
title = {Less is More: Efficient Image Vectorization with Adaptive Parameterization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18166-18175}
} | Image vectorization aims to convert raster images to vector ones, allowing for easy scaling and editing.Existing works mainly rely on preset parameters (i.e., a fixed number of paths and control points), ignoring the complexity of the image and posing significant challenges to practical applications.We demonstrate that such an assumption is often incorrect, as the preset paths or control points may be neither essential nor enough to achieve accurate and editable vectorization results.Based on this key insight, in this paper, we propose AdaVec, an efficient image vectorization method with adaptive parametrization, where the paths and control points can be adjusted dynamically based on the complexity of the input raster image.In particular, we first decompose the input raster image into a set of pure-colored layers that are aligned with human perception.For each layer with varying shape complexity, we propose a novel allocation mechanism to adaptively adjust the control point distribution.We further adopt a differentiable rendering process to compose and optimize the shape and color parameters of each layer iteratively.Extensive experiments demonstrate that AdaVec outperforms the baselines qualitatively and quantitatively, in terms of computational efficiency, vectorization accuracy, and editing flexibility. | [
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245 | FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning | [
"Gongxi Zhu",
"Donghao Li",
"Hanlin Gu",
"Yuan Yao",
"Lixin Fan",
"Yuxing Han"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_FedMIA_An_Effective_Membership_Inference_Attack_Exploiting_All_for_One_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_FedMIA_An_Effective_Membership_Inference_Attack_Exploiting_All_for_One_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_FedMIA_An_Effective_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2402.06289 | @InProceedings{Zhu_2025_CVPR,
author = {Zhu, Gongxi and Li, Donghao and Gu, Hanlin and Yao, Yuan and Fan, Lixin and Han, Yuxing},
title = {FedMIA: An Effective Membership Inference Attack Exploiting ''All for One'' Principle in Federated Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20643-20653}
} | Federated Learning (FL) is a promising approach for training machine learning models on decentralized data while preserving privacy. However, privacy risks, particularly Membership Inference Attacks (MIAs), which aim to determine whether a specific data point belongs to a target client's training set, remain a significant concern. Existing methods for implementing MIAs in FL primarily analyze updates from the target client, focusing on metrics such as loss, gradient norm, and gradient difference. However, these methods fail to leverage updates from non-target clients, potentially underutilizing available information.In this paper, we first formulate a one-tailed likelihood-ratio hypothesis test based on the likelihood of updates from non-target clients. Building upon this formulation, we introduce a three-stage Membership Inference Attack (MIA) method, called FedMIA, which follows the "all for one"--leveraging updates from all clients across multiple communication rounds to enhance MIA effectiveness. Both theoretical analysis and extensive experimental results demonstrate that FedMIA outperforms existing MIAs in both classification and generative tasks. Additionally, it can be integrated as an extension to existing methods and is robust against various defense strategies, Non-IID data, and different federated structures. Our code is available in https://github.com/Liar-Mask/FedMIA. | [
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] |
246 | Erase Diffusion: Empowering Object Removal Through Calibrating Diffusion Pathways | [
"Yi Liu",
"Hao Zhou",
"Benlei Cui",
"Wenxiang Shang",
"Ran Lin"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Erase_Diffusion_Empowering_Object_Removal_Through_Calibrating_Diffusion_Pathways_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Erase_Diffusion_Empowering_Object_Removal_Through_Calibrating_Diffusion_Pathways_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Erase_Diffusion_Empowering_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.07026 | @InProceedings{Liu_2025_CVPR,
author = {Liu, Yi and Zhou, Hao and Cui, Benlei and Shang, Wenxiang and Lin, Ran},
title = {Erase Diffusion: Empowering Object Removal Through Calibrating Diffusion Pathways},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2418-2427}
} | Erase inpainting, or object removal, aims to precisely remove target objects within masked regions while preserving the overall consistency of the surrounding content. Despite diffusion-based methods have made significant strides in the field of image inpainting, challenges remain regarding the emergence of unexpected objects or artifacts. We assert that the inexact diffusion pathways established by existing standard optimization paradigms constrain the efficacy of object removal. To tackle these challenges, we propose a novel Erase Diffusion, termed EraDiff, aimed at unleashing the potential power of standard diffusion in the context of object removal. In contrast to standard diffusion, the EraDiff adapts both the optimization paradigm and the network to improve the coherence and elimination of the erasure results.We first introduce a Chain-Rectifying Optimization (CRO) paradigm, a sophisticated diffusion process specifically designed to align with the objectives of erasure. This paradigm establishes innovative diffusion transition pathways that simulate the gradual elimination of objects during optimization, allowing the model to accurately capture the intent of object removal. Furthermore, to mitigate deviations caused by artifacts during the sampling pathways, we develop a simple yet effective Self-Rectifying Attention (SRA) mechanism. The SRA calibrates the sampling pathways by altering self-attention activation, allowing the model to effectively bypass artifacts while further enhancing the coherence of the generated content. With this design, our proposed EraDiff achieves state-of-the-art performance on the OpenImages V5 dataset and demonstrates significant superiority in real-world scenarios. | [
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247 | Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis | [
"Arpita Chowdhury",
"Dipanjyoti Paul",
"Zheda Mai",
"Jianyang Gu",
"Ziheng Zhang",
"Kazi Sajeed Mehrab",
"Elizabeth G. Campolongo",
"Daniel Rubenstein",
"Charles V. Stewart",
"Anuj Karpatne",
"Tanya Berger-Wolf",
"Yu Su",
"Wei-Lun Chao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chowdhury_Prompt-CAM_Making_Vision_Transformers_Interpretable_for_Fine-Grained_Analysis_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chowdhury_Prompt-CAM_Making_Vision_Transformers_Interpretable_for_Fine-Grained_Analysis_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chowdhury_Prompt-CAM_Making_Vision_CVPR_2025_supplemental.pdf | null | @InProceedings{Chowdhury_2025_CVPR,
author = {Chowdhury, Arpita and Paul, Dipanjyoti and Mai, Zheda and Gu, Jianyang and Zhang, Ziheng and Mehrab, Kazi Sajeed and Campolongo, Elizabeth G. and Rubenstein, Daniel and Stewart, Charles V. and Karpatne, Anuj and Berger-Wolf, Tanya and Su, Yu and Chao, Wei-Lun},
title = {Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4375-4385}
} | We present a simple approach to make pre-trained Vision Transformers (ViTs) interpretable for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as bird species. Pre-trained ViTs, such as DINO, have demonstrated remarkable capabilities in extracting localized, discriminative features. However, saliency maps like Grad-CAM often fail to identify these traits, producing blurred, coarse heatmaps that highlight entire objects instead. We propose a novel approach, Prompt Class Attention Map (Prompt-CAM), to address this limitation. Prompt-CAM learns class-specific prompts for a pre-trained ViT and uses the corresponding outputs for classification. To correctly classify an image, the true-class prompt must attend to unique image patches not present in other classes' images (i.e., traits). As a result, the true class's multi-head attention maps reveal traits and their locations. Implementation-wise, Prompt-CAM is almost a "free lunch," requiring only a modification to the prediction head of Visual Prompt Tuning (VPT). This makes Prompt-CAM easy to train and apply, in stark contrast to other interpretable methods that require designing specific models and training processes. Extensive empirical studies on a dozen datasets from various domains (e.g., birds, fishes, insects, fungi, flowers, food, and cars) validate the superior interpretation capability of Prompt-CAM. The source code and demo are available at https://github.com/Imageomics/Prompt_CAM. | [
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248 | Instruction-based Image Manipulation by Watching How Things Move | [
"Mingdeng Cao",
"Xuaner Zhang",
"Yinqiang Zheng",
"Zhihao Xia"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Cao_Instruction-based_Image_Manipulation_by_Watching_How_Things_Move_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Cao_Instruction-based_Image_Manipulation_by_Watching_How_Things_Move_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cao_Instruction-based_Image_Manipulation_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.12087 | @InProceedings{Cao_2025_CVPR,
author = {Cao, Mingdeng and Zhang, Xuaner and Zheng, Yinqiang and Xia, Zhihao},
title = {Instruction-based Image Manipulation by Watching How Things Move},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2704-2713}
} | This paper introduces a novel dataset construction pipeline that samples pairs of frames from videos and uses multimodal large language models (MLLMs) to generate editing instructions for training instruction-based image manipulation models. Video frames inherently preserve the identity of subjects and scenes, ensuring consistent content preservation during editing. Additionally, video data captures diverse, natural dynamics--such as non-rigid subject motion and complex camera movements--that are difficult to model otherwise, making it an ideal source for scalable dataset construction. Using this approach, we create a new dataset to train InstructMove, a model capable of instruction-based complex manipulations that are difficult to achieve with synthetically generated datasets. Our model demonstrates state-of-the-art performance in tasks such as adjusting subject poses, rearranging elements, and altering camera perspectives. | [
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249 | DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework | [
"Henrique Morimitsu",
"Xiaobin Zhu",
"Roberto M. Cesar",
"Xiangyang Ji",
"Xu-Cheng Yin"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Morimitsu_DPFlow_Adaptive_Optical_Flow_Estimation_with_a_Dual-Pyramid_Framework_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Morimitsu_DPFlow_Adaptive_Optical_Flow_Estimation_with_a_Dual-Pyramid_Framework_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Morimitsu_DPFlow_Adaptive_Optical_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.14880 | @InProceedings{Morimitsu_2025_CVPR,
author = {Morimitsu, Henrique and Zhu, Xiaobin and Cesar, Roberto M. and Ji, Xiangyang and Yin, Xu-Cheng},
title = {DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17810-17820}
} | Optical flow estimation is essential for video processing tasks, such as restoration and action recognition. The quality of videos is constantly increasing, with current standards reaching 8K resolution. However, optical flow methods are usually designed for low resolution and do not generalize to large inputs due to their rigid architectures. They adopt downscaling or input tiling to reduce the input size, causing a loss of details and global information. There is also a lack of optical flow benchmarks to judge the actual performance of existing methods on high-resolution samples. Previous works only conducted qualitative high-resolution evaluations on hand-picked samples. This paper fills this gap in optical flow estimation in two ways. We propose DPFlow, an adaptive optical flow architecture capable of generalizing up to 8K resolution inputs while trained with only low-resolution samples. We also introduce Kubric-NK, a new benchmark for evaluating optical flow methods with input resolutions ranging from 1K to 8K. Our high-resolution evaluation pushes the boundaries of existing methods and reveals new insights about their generalization capabilities. Extensive experimental results show that DPFlow achieves state-of-the-art results on the MPI-Sintel, KITTI 2015, Spring, and other high-resolution benchmarks. The code and dataset are available at https://github.com/hmorimitsu/ptlflow/tree/main/ptlflow/models/dpflow. | [
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250 | DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning | [
"Xiao-Hui Li",
"Fei Yin",
"Cheng-Lin Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Li_DocSAM_Unified_Document_Image_Segmentation_via_Query_Decomposition_and_Heterogeneous_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Li_DocSAM_Unified_Document_Image_Segmentation_via_Query_Decomposition_and_Heterogeneous_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_DocSAM_Unified_Document_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2504.04085 | @InProceedings{Li_2025_CVPR,
author = {Li, Xiao-Hui and Yin, Fei and Liu, Cheng-Lin},
title = {DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15021-15032}
} | Document image segmentation is crucial in document analysis and recognition but remains challenging due to the heterogeneity of document formats and diverse segmentation tasks. Existing methods often treat these tasks separately, leading to limited generalization and resource wastage.This paper introduces DocSAM, a transformer-based unified framework for various document image segmentation tasks, including document layout analysis, multi-granularity text segmentation, and table structure recognition by modelling these tasks as a combination of instance and semantic segmentation.Specifically, DocSAM uses a Sentence BERT to map category names from each dataset into semantic queries of the same dimension as instance queries. These queries interact through attention mechanisms and are cross-attended with image features to predict instance and semantic segmentation masks. To predict instance categories, instance queries are dot-producted with semantic queries, and scores are normalized using softmax.As a result, DocSAM can be jointly trained on heterogeneous datasets, enhancing robustness and generalization while reducing computing and storage resources. Comprehensive evaluations show that DocSAM outperforms existing methods in accuracy, efficiency, and adaptability, highlighting its potential for advancing document image understanding and segmentation in various applications. | [
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251 | Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints | [
"Yuhao Zhou",
"Yuxin Tian",
"Jindi Lv",
"Mingjia Shi",
"Yuanxi Li",
"Qing Ye",
"Shuhao Zhang",
"Jiancheng Lv"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Ferret_An_Efficient_Online_Continual_Learning_Framework_under_Varying_Memory_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Ferret_An_Efficient_Online_Continual_Learning_Framework_under_Varying_Memory_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Ferret_An_Efficient_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.12053 | @InProceedings{Zhou_2025_CVPR,
author = {Zhou, Yuhao and Tian, Yuxin and Lv, Jindi and Shi, Mingjia and Li, Yuanxi and Ye, Qing and Zhang, Shuhao and Lv, Jiancheng},
title = {Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4850-4861}
} | In the realm of high-frequency data streams, achieving real-time learning within varying memory constraints is paramount. This paper presents Ferret, a comprehensive framework designed to enhance online accuracy of Online Continual Learning (OCL) algorithms while dynamically adapting to varying memory budgets.Ferret employs a fine-grained pipeline parallelism strategy combined with an iterative gradient compensation algorithm, ensuring seamless handling of high-frequency data with minimal latency, and effectively counteracting the challenge of stale gradients in parallel training. To adapt to varying memory budgets, its automated model partitioning and pipeline planning optimizes performance regardless of memory limitations. Extensive experiments across 20 benchmarks and 5 integrated OCL algorithms show Ferret's remarkable efficiency, achieving up to 3.7x lower memory overhead to reach the same online accuracy compared to competing methods.Furthermore, Ferret consistently outperforms these methods across diverse memory budgets, underscoring its superior adaptability. These findings position Ferret as a premier solution for efficient and adaptive OCL framework in real-time environments. | [
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252 | Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling | [
"Junha Hyung",
"Kinam Kim",
"Susung Hong",
"Min-Jung Kim",
"Jaegul Choo"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Hyung_Spatiotemporal_Skip_Guidance_for_Enhanced_Video_Diffusion_Sampling_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Hyung_Spatiotemporal_Skip_Guidance_for_Enhanced_Video_Diffusion_Sampling_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hyung_Spatiotemporal_Skip_Guidance_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.18664 | @InProceedings{Hyung_2025_CVPR,
author = {Hyung, Junha and Kim, Kinam and Hong, Susung and Kim, Min-Jung and Choo, Jaegul},
title = {Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11006-11015}
} | Diffusion models have emerged as a powerful tool for generating high-quality images, videos, and 3D content. While sampling guidance techniques like CFG improve quality, they reduce diversity and motion. Autoguidance mitigates these issues but demands extra weak model training, limiting its practicality for large-scale models. In this work, we introduce Spatiotemporal Skip Guidance (STG), a simple training-free sampling guidance method for enhancing transformer-based video diffusion models. STG employs an implicit weak model via self-perturbation, avoiding the need for external models or additional training. By selectively skipping spatiotemporal layers, STG produces an aligned, degraded version of the original model to boost sample quality without compromising diversity or dynamic degree. Our contributions include: (1) introducing STG as an efficient, high-performing guidance technique for video diffusion models, (2) eliminating the need for auxiliary models by simulating a weak model through layer skipping, and (3) ensuring quality-enhanced guidance without compromising sample diversity or dynamics unlike CFG. For additional results, visit https://junhahyung.github.io/STGuidance. | [
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] |
253 | VidComposition: Can MLLMs Analyze Compositions in Compiled Videos? | [
"Yunlong Tang",
"Junjia Guo",
"Hang Hua",
"Susan Liang",
"Mingqian Feng",
"Xinyang Li",
"Rui Mao",
"Chao Huang",
"Jing Bi",
"Zeliang Zhang",
"Pooyan Fazli",
"Chenliang Xu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Tang_VidComposition_Can_MLLMs_Analyze_Compositions_in_Compiled_Videos_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_VidComposition_Can_MLLMs_Analyze_Compositions_in_Compiled_Videos_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_VidComposition_Can_MLLMs_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.10979 | @InProceedings{Tang_2025_CVPR,
author = {Tang, Yunlong and Guo, Junjia and Hua, Hang and Liang, Susan and Feng, Mingqian and Li, Xinyang and Mao, Rui and Huang, Chao and Bi, Jing and Zhang, Zeliang and Fazli, Pooyan and Xu, Chenliang},
title = {VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8490-8500}
} | The advancement of Multimodal Large Language Models (MLLMs) has enabled significant progress in multimodal understanding, expanding their capacity to analyze video content. However, existing evaluation benchmarks for MLLMs primarily focus on abstract video comprehension, lacking a detailed assessment of their ability to understand video compositions, the nuanced interpretation of how visual elements combine and interact within highly compiled video contexts. We introduce VidComposition, a new benchmark specifically designed to evaluate the video composition understanding capabilities of MLLMs using carefully curated compiled videos and cinematic-level annotations.VidComposition includes 982 videos with 1706 multiple-choice questions, covering various compositional aspects such as camera movement, angle, shot size, narrative structure, character actions and emotions, etc. Our comprehensive evaluation of 33 open-source and proprietary MLLMs reveals a significant performance gap between human and model capabilities. This highlights the limitations of current MLLMs in understanding complex, compiled video compositions and offers insights into areas for further improvement. Our benchmark is publicly available at https://yunlong10.github.io/VidComposition/ | [
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254 | SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE | [
"Yongwei Chen",
"Yushi Lan",
"Shangchen Zhou",
"Tengfei Wang",
"Xingang Pan"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chen_SAR3D_Autoregressive_3D_Object_Generation_and_Understanding_via_Multi-scale_3D_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_SAR3D_Autoregressive_3D_Object_Generation_and_Understanding_via_Multi-scale_3D_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_SAR3D_Autoregressive_3D_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.16856 | @InProceedings{Chen_2025_CVPR,
author = {Chen, Yongwei and Lan, Yushi and Zhou, Shangchen and Wang, Tengfei and Pan, Xingang},
title = {SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28371-28382}
} | Autoregressive models have demonstrated remarkable success across various fields, from large language models (LLMs) to large multimodal models (LMMs) and 2D content generation, moving closer to artificial general intelligence (AGI). Despite these advances, applying autoregressive approaches to 3D object generation and understanding remains largely unexplored. This paper introduces Scale AutoRegressive 3D (SAR3D), a novel framework that leverages a multi-scale 3D vector-quantized variational autoencoder (VQVAE) to tokenize 3D objects for efficient autoregressive generation and detailed understanding. By predicting the next scale in a multi-scale latent representation instead of the next single token, SAR3D reduces generation time significantly, achieving fast 3D object generation in just 0.82 seconds on an A6000 GPU. Additionally, given the tokens enriched with hierarchical 3D-aware information, we finetune a pretrained LLM on them, enabling multimodal comprehension of 3D content.Our experiments show that SAR3D surpasses current 3D generation methods in both speed and quality and allows LLMs to interpret and caption 3D models comprehensively. | [
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255 | Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation | [
"Ying Jin",
"Jinlong Peng",
"Qingdong He",
"Teng Hu",
"Jiafu Wu",
"Hao Chen",
"Haoxuan Wang",
"Wenbing Zhu",
"Mingmin Chi",
"Jun Liu",
"Yabiao Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Jin_Dual-Interrelated_Diffusion_Model_for_Few-Shot_Anomaly_Image_Generation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Jin_Dual-Interrelated_Diffusion_Model_for_Few-Shot_Anomaly_Image_Generation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jin_Dual-Interrelated_Diffusion_Model_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2408.13509 | @InProceedings{Jin_2025_CVPR,
author = {Jin, Ying and Peng, Jinlong and He, Qingdong and Hu, Teng and Wu, Jiafu and Chen, Hao and Wang, Haoxuan and Zhu, Wenbing and Chi, Mingmin and Liu, Jun and Wang, Yabiao},
title = {Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30420-30429}
} | The performance of anomaly inspection in industrial manufacturing is constrained by the scarcity of anomaly data. To overcome this challenge, researchers have started employing anomaly generation approaches to augment the anomaly dataset. However, existing anomaly generation methods suffer from limited diversity in the generated anomalies and struggle to achieve a seamless blending of this anomaly with the original image. Moreover, the generated mask is usually not aligned with the generated anomaly. In this paper, we overcome these challenges from a new perspective, simultaneously generating a pair of the overall image and the corresponding anomaly part. We propose DualAnoDiff, a novel diffusion-based few-shot anomaly image generation model, which can generate diverse and realistic anomaly images by using a dual-interrelated diffusion model, where one of them is employed to generate the whole image while the other one generates the anomaly part. Moreover, we extract background and shape information to mitigate the distortion and blurriness phenomenon in few-shot image generation. Extensive experiments demonstrate the superiority of our proposed model over state-of-the-art methods in terms of diversity, realism and the accuracy of mask. Overall, our approach significantly improves the performance of downstream anomaly inspection tasks, including anomaly detection, anomaly localization, and anomaly classification tasks. Code will be made available. | [
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] |
256 | ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models | [
"Yahan Tu",
"Rui Hu",
"Jitao Sang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Tu_ODE_Open-Set_Evaluation_of_Hallucinations_in_Multimodal_Large_Language_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Tu_ODE_Open-Set_Evaluation_of_Hallucinations_in_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tu_ODE_Open-Set_Evaluation_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2409.09318 | @InProceedings{Tu_2025_CVPR,
author = {Tu, Yahan and Hu, Rui and Sang, Jitao},
title = {ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19836-19845}
} | Hallucination poses a persistent challenge for multimodal large language models (MLLMs). However, existing benchmarks for evaluating hallucinations are generally static, which may overlook the potential risk of data contamination. To address this issue, we propose ODE, an open-set, dynamic protocol designed to evaluate object hallucinations in MLLMs at both the existence and attribute levels. ODE employs a graph-based structure to represent real-world object concepts, their attributes, and the distributional associations between them. This structure facilitates the extraction of concept combinations based on diverse distributional criteria, generating varied samples for structured queries that evaluate hallucinations in both generative and discriminative tasks. Through the generation of new samples, dynamic concept combinations, and varied distribution frequencies, ODE mitigates the risk of data contamination and broadens the scope of evaluation. This protocol is applicable to both general and specialized scenarios, including those with limited data. Experimental results demonstrate the effectiveness of our protocol, revealing that MLLMs exhibit higher hallucination rates when evaluated with ODE-generated samples, which indicates potential data contamination. Furthermore, these generated samples aid in analyzing hallucination patterns and fine-tuning models, offering an effective approach to mitigating hallucinations in MLLMs. | [
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] |
257 | Self-Supervised Learning for Color Spike Camera Reconstruction | [
"Yanchen Dong",
"Ruiqin Xiong",
"Xiaopeng Fan",
"Zhaofei Yu",
"Yonghong Tian",
"Tiejun Huang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Self-Supervised_Learning_for_Color_Spike_Camera_Reconstruction_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Self-Supervised_Learning_for_Color_Spike_Camera_Reconstruction_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dong_Self-Supervised_Learning_for_CVPR_2025_supplemental.pdf | null | @InProceedings{Dong_2025_CVPR,
author = {Dong, Yanchen and Xiong, Ruiqin and Fan, Xiaopeng and Yu, Zhaofei and Tian, Yonghong and Huang, Tiejun},
title = {Self-Supervised Learning for Color Spike Camera Reconstruction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6231-6240}
} | Spike camera is a kind of neuromorphic camera with ultra-high temporal resolution, which can capture dynamic scenes by continuously firing spike signals. To capture color information, a color filter array (CFA) is employed on the sensor of the spike camera, resulting in Bayer-pattern spike streams. How to restore high-quality color images from the binary spike signals remains challenging. In this paper, we propose a motion-guided reconstruction method for spike cameras with CFA, utilizing color layout and estimated motion information. Specifically, we develop a joint motion estimation pipeline for the Bayer-pattern spike stream, exploiting the motion consistency of channels. We propose to estimate the missing pixels of each color channel according to temporally neighboring pixels of the corresponding color along the motion trajectory. As the spike signals are read out at discrete time points, there is quantization noise that impacts the image quality. Thus, we analyze the correlation of the noise in spatial and temporal domains and propose a self-supervised network utilizing a masked spike encoder to handle the noise. Experiments on real-world captured Bayer-pattern spike streams show that our method can restore color images with better visual quality, compared with state-of-the-art methods. The source codes are available at https://github.com/csycdong/SSL-CSC. | [
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] |
258 | Interactive Medical Image Analysis with Concept-based Similarity Reasoning | [
"Ta Duc Huy",
"Sen Kim Tran",
"Phan Nguyen",
"Nguyen Hoang Tran",
"Tran Bao Sam",
"Anton van den Hengel",
"Zhibin Liao",
"Johan W. Verjans",
"Minh-Son To",
"Vu Minh Hieu Phan"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Huy_Interactive_Medical_Image_Analysis_with_Concept-based_Similarity_Reasoning_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Huy_Interactive_Medical_Image_Analysis_with_Concept-based_Similarity_Reasoning_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huy_Interactive_Medical_Image_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2503.06873 | @InProceedings{Huy_2025_CVPR,
author = {Huy, Ta Duc and Tran, Sen Kim and Nguyen, Phan and Tran, Nguyen Hoang and Sam, Tran Bao and van den Hengel, Anton and Liao, Zhibin and Verjans, Johan W. and To, Minh-Son and Phan, Vu Minh Hieu},
title = {Interactive Medical Image Analysis with Concept-based Similarity Reasoning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30797-30806}
} | The ability to interpret and intervene model decisions is important for the adoption of computer-aided diagnosis methods in clinical workflows. Recent concept-based methods link the model predictions with interpretable concepts and modify their activation scores to interact with the model. However, these concepts are at the image level, which hinders the model from pinpointing the exact patches the concepts are activated. Alternatively, prototype-based methods learn representations from training image patches and compare these with test image patches, using the similarity scores for final class prediction. However, interpreting the underlying concepts of these patches can be challenging and often necessitates post-hoc guesswork. To address this issue, this paper introduces the novel Concept-based Similarity Reasoning network (CSR), which offers (i) patch-level prototype with intrinsic concept interpretation, and (ii) spatial interactivity. First, the proposed CSR provides localized explanation by grounding prototypes of each concept on image regions. Second, our model introduces novel spatial-level interaction, allowing doctors to engage directly with specific image areas, making it an intuitive and transparent tool for medical imaging. CSR improves upon prior state-of-the-art interpretable methods by up to 4.% across three biomedical datasets. | [
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] |
259 | From Elements to Design: A Layered Approach for Automatic Graphic Design Composition | [
"Jiawei Lin",
"Shizhao Sun",
"Danqing Huang",
"Ting Liu",
"Ji Li",
"Jiang Bian"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lin_From_Elements_to_Design_A_Layered_Approach_for_Automatic_Graphic_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_From_Elements_to_Design_A_Layered_Approach_for_Automatic_Graphic_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_From_Elements_to_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.19712 | @InProceedings{Lin_2025_CVPR,
author = {Lin, Jiawei and Sun, Shizhao and Huang, Danqing and Liu, Ting and Li, Ji and Bian, Jiang},
title = {From Elements to Design: A Layered Approach for Automatic Graphic Design Composition},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8128-8137}
} | In this work, we investigate automatic design composition from multimodal graphic elements. Although recent studies have developed various generative models for graphic design, they usually face the following limitations: they only focus on certain subtasks and are far from achieving the design composition task; they do not consider the hierarchical information of graphic designs during the generation process. To tackle these issues, we introduce the layered design principle into Large Multimodal Models (LMMs) and propose a novel approach, called LaDeCo, to accomplish this challenging task. Specifically, LaDeCo first performs layer planning for a given element set, dividing the input elements into different semantic layers according to their contents. Based on the planning results, it subsequently predicts element attributes that control the design composition in a layer-wise manner, and includes the rendered image of previously generated layers into the context. With this insightful design, LaDeCo decomposes the difficult task into smaller manageable steps, making the generation process smoother and clearer. The experimental results demonstrate the effectiveness of LaDeCo in design composition. Furthermore, we show that LaDeCo enables some interesting applications in graphic design, such as resolution adjustment, design decoration, design variation, etc. In addition, it even outperforms the specialized models in some design subtasks without any task-specific training. | [
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260 | h-Edit: Effective and Flexible Diffusion-Based Editing via Doob's h-Transform | [
"Toan Nguyen",
"Kien Do",
"Duc Kieu",
"Thin Nguyen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_h-Edit_Effective_and_Flexible_Diffusion-Based_Editing_via_Doobs_h-Transform_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_h-Edit_Effective_and_Flexible_Diffusion-Based_Editing_via_Doobs_h-Transform_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_h-Edit_Effective_and_CVPR_2025_supplemental.pdf | null | @InProceedings{Nguyen_2025_CVPR,
author = {Nguyen, Toan and Do, Kien and Kieu, Duc and Nguyen, Thin},
title = {h-Edit: Effective and Flexible Diffusion-Based Editing via Doob's h-Transform},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28490-28501}
} | We introduce a theoretical framework for diffusion-based image editing by formulating it as a reverse-time bridge modeling problem. This approach modifies the backward process of a pretrained diffusion model to construct a bridge that converges to an implicit distribution associated with the editing target at time 0. Building on this framework, we propose h-Edit, a novel editing method that utilizes Doob's h-transform and Langevin Monte Carlo to decompose the update of an intermediate edited sample into two components: a "reconstruction" term and an "editing" term. This decomposition provides flexibility, allowing the reconstruction term to be computed via existing inversion techniques and enabling the combination of multiple editing terms to handle complex editing tasks. To our knowledge, h-Edit is the first training-free method capable of performing simultaneous text-guided and reward-model-based editing. Extensive experiments, both quantitative and qualitative, show that h-Edit outperforms state-of-the-art baselines in terms of editing effectiveness and faithfulness. | [
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] |
261 | Masking meets Supervision: A Strong Learning Alliance | [
"Byeongho Heo",
"Taekyung Kim",
"Sangdoo Yun",
"Dongyoon Han"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Heo_Masking_meets_Supervision_A_Strong_Learning_Alliance_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Heo_Masking_meets_Supervision_A_Strong_Learning_Alliance_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Heo_Masking_meets_Supervision_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2306.11339 | @InProceedings{Heo_2025_CVPR,
author = {Heo, Byeongho and Kim, Taekyung and Yun, Sangdoo and Han, Dongyoon},
title = {Masking meets Supervision: A Strong Learning Alliance},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20447-20457}
} | Pre-training with random masked inputs has emerged as a novel trend in self-supervised training. However, supervised learning still faces a challenge in adopting masking augmentations, primarily due to unstable training. In this paper, we propose a novel way to involve masking augmentations dubbed Masked Sub-branch (MaskSub). MaskSub consists of the main-branch and sub-branch, the latter being a part of the former. The main-branch undergoes conventional training recipes, while the sub-branch merits intensive masking augmentations, during training. MaskSub tackles the challenge by mitigating adverse effects through a relaxed loss function similar to a self-distillation loss. Our analysis shows that MaskSub improves performance, with the training loss converging faster than in standard training, which suggests our method stabilizes the training process. We further validate MaskSub across diverse training scenarios and models, including DeiT-III training, MAE finetuning, CLIP finetuning, BERT training, and hierarchical architectures (ResNet and Swin Transformer). Our results show that MaskSub consistently achieves impressive performance gains across all the cases. MaskSub provides a practical and effective solution for introducing additional regularization under various training recipes. Code available at https://github.com/naver-ai/augsub | [
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262 | DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation | [
"Wang Zhao",
"Yan-Pei Cao",
"Jiale Xu",
"Yuejiang Dong",
"Ying Shan"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_DI-PCG_Diffusion-based_Efficient_Inverse_Procedural_Content_Generation_for_High-quality_3D_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_DI-PCG_Diffusion-based_Efficient_Inverse_Procedural_Content_Generation_for_High-quality_3D_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_DI-PCG_Diffusion-based_Efficient_CVPR_2025_supplemental.pdf | null | @InProceedings{Zhao_2025_CVPR,
author = {Zhao, Wang and Cao, Yan-Pei and Xu, Jiale and Dong, Yuejiang and Shan, Ying},
title = {DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11061-11072}
} | Procedural Content Generation (PCG) is powerful in creating high-quality 3D contents, yet controlling it to produce desired shapes is difficult and often requires extensive parameter tuning. Inverse Procedural Content Generation aims to automatically find the best parameters under the input condition. However, existing sampling-based and neural network-based methods still suffer from numerous sample iterations or limited controllability. In this work, we present DI-PCG, a novel and efficient method for Inverse PCG from general image conditions. At its core is a lightweight diffusion transformer model, where PCG parameters are directly treated as the denoising target and the observed images as conditions to control parameter generation. DI-PCG is efficient and effective. With only 7.6M network parameters and 30 GPU hours to train, it demonstrates superior performance in recovering parameters accurately, and generalizing well to in-the-wild images. Quantitative and qualitative experiment results validate the effectiveness of DI-PCG in inverse PCG and image-to-3D generation tasks. DI-PCG offers a promising approach for efficient inverse PCG and represents a valuable exploration step towards a 3D generation path that models how to construct a 3D asset using parametric models. | [
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263 | SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval and Routing in Long-Form Video Analysis | [
"Junho Kim",
"Hyunjun Kim",
"Hosu Lee",
"Yong Man Ro"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Kim_SALOVA_Segment-Augmented_Long_Video_Assistant_for_Targeted_Retrieval_and_Routing_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_SALOVA_Segment-Augmented_Long_Video_Assistant_for_Targeted_Retrieval_and_Routing_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_SALOVA_Segment-Augmented_Long_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.16173 | @InProceedings{Kim_2025_CVPR,
author = {Kim, Junho and Kim, Hyunjun and Lee, Hosu and Ro, Yong Man},
title = {SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval and Routing in Long-Form Video Analysis},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3352-3362}
} | Despite advances in Large Multi-modal Models, applying them to long and untrimmed video content remains challenging due to limitations in context length and substantial memory overhead. These constraints often lead to significant information loss and reduced relevance in the model responses. With the exponential growth of video data across web platforms, understanding long-form video is crucial for advancing generalized intelligence. In this paper, we introduce SALOVA: Segment-Augmented LOng Video Assistant, a novel video-LLM framework designed to enhance the comprehension of lengthy video content through targeted retrieval process. We address two main challenges to achieve it: (i) We present the SceneWalk dataset, a high-quality collection of 87.8K long videos, each densely captioned at the segment level to enable models to capture scene continuity and maintain rich descriptive context. (ii) We develop robust architectural designs integrating dynamic routing mechanism and spatio-temporal projector to efficiently retrieve and process relevant video segments based on user queries. Our framework mitigates the limitations of current video-LMMs by allowing for precise identification and retrieval of relevant video segments in response to queries, thereby improving the contextual relevance of the generated responses. Through extensive experiments, SALOVA demonstrates enhanced capability in processing complex long-form videos, showing significant capability to maintain contextual integrity across extended sequences. | [
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264 | Notes-guided MLLM Reasoning: Enhancing MLLM with Knowledge and Visual Notes for Visual Question Answering | [
"Wenlong Fang",
"Qiaofeng Wu",
"Jing Chen",
"Yun Xue"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Fang_Notes-guided_MLLM_Reasoning_Enhancing_MLLM_with_Knowledge_and_Visual_Notes_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_Notes-guided_MLLM_Reasoning_Enhancing_MLLM_with_Knowledge_and_Visual_Notes_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_Notes-guided_MLLM_Reasoning_CVPR_2025_supplemental.pdf | null | @InProceedings{Fang_2025_CVPR,
author = {Fang, Wenlong and Wu, Qiaofeng and Chen, Jing and Xue, Yun},
title = {Notes-guided MLLM Reasoning: Enhancing MLLM with Knowledge and Visual Notes for Visual Question Answering},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19597-19607}
} | The knowledge-based visual question answering (KB-VQA) task involves using external knowledge about the image to assist reasoning. Building on the impressive performance of multimodal large language model (MLLM), recent methods have commenced leveraging MLLM as an implicit knowledge base for reasoning. However, the direct employment of MLLM with raw external knowledge might result in reasoning errors due to misdirected knowledge information. Additionally, MLLM may lack fine-grained perception of visual features, which can result in hallucinations during reasoning. To address these challenges, we propose Notes-guided MLLM Reasoning (NoteMR), a novel framework that guides MLLM in better reasoning by utilizing knowledge notes and visual notes. Specifically, we initially obtain explicit knowledge from an external knowledge base. Then, this explicit knowledge, combined with images, is used to assist the MLLM in generating knowledge notes. These notes are designed to filter explicit knowledge and identify relevant internal implicit knowledge within the MLLM. We then identify highly correlated regions between the images and knowledge notes, retaining them as image notes to enhance the model's fine-grained perception, thereby mitigating MLLM induced hallucinations. Finally, both notes are fed into the MLLM, enabling a more comprehensive understanding of the image-question pair and enhancing the model's reasoning capabilities. Our method achieves state-of-the-art performance on the OK-VQA and A-OKVQA datasets, demonstrating its robustness and effectiveness across diverse VQA scenarios. | [
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] |
265 | Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized? | [
"Jianyang Xie",
"Yitian Zhao",
"Yanda Meng",
"He Zhao",
"Anh Nguyen",
"Yalin Zheng"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Are_Spatial-Temporal_Graph_Convolution_Networks_for_Human_Action_Recognition_Over-Parameterized_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Are_Spatial-Temporal_Graph_Convolution_Networks_for_Human_Action_Recognition_Over-Parameterized_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Are_Spatial-Temporal_Graph_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2505.10679 | @InProceedings{Xie_2025_CVPR,
author = {Xie, Jianyang and Zhao, Yitian and Meng, Yanda and Zhao, He and Nguyen, Anh and Zheng, Yalin},
title = {Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24309-24319}
} | Spatial-temporal graph convolutional networks (ST-GCNs) showcase impressive performance in skeleton-based human action recognition (HAR). However, despite the development of numerous models, their recognition performance does not differ significantly after aligning the input settings. With this observation, we hypothesize that ST-GCNs are over-parameterized for HAR, a conjecture subsequently confirmed through experiments employing the lottery ticket hypothesis. Additionally, a novel sparse ST-GCNs generator is proposed, which trains a sparse architecture from a randomly initialized dense network while maintaining comparable performance levels to the dense components. Moreover, we generate multi-level sparsity ST-GCNs by integrating sparse structures at various sparsity levels and demonstrate that the assembled model yields a significant enhancement in HAR performance. Thorough experiments on four datasets, including NTU-RGB+D 60(120), Kinetics-400, and FineGYM, demonstrate that the proposed sparse ST-GCNs can achieve comparable performance to their dense components. Even with 95% fewer parameters, the sparse ST-GCNs exhibit a degradation of <1% in top-1 accuracy. Meanwhile, the multi-level sparsity ST-GCNs, which require only 66% of the parameters of the dense ST-GCNs, demonstrate an improvement of >1% in top-1 accuracy. The code is available at https://github.com/davelailai/Sparse-ST-GCN. | [
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266 | DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers | [
"Li Ren",
"Chen Chen",
"Liqiang Wang",
"Kien Hua"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ren_DA-VPT_Semantic-Guided_Visual_Prompt_Tuning_for_Vision_Transformers_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_DA-VPT_Semantic-Guided_Visual_Prompt_Tuning_for_Vision_Transformers_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_DA-VPT_Semantic-Guided_Visual_CVPR_2025_supplemental.pdf | null | @InProceedings{Ren_2025_CVPR,
author = {Ren, Li and Chen, Chen and Wang, Liqiang and Hua, Kien},
title = {DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4353-4363}
} | Visual Prompt Tuning (VPT) has become a promising solution for Parameter-Efficient Fine-Tuning (PEFT) approach for Vision Transformer (ViT) models by partially fine-tuning learnable tokens while keeping most model parameters frozen. Recent research has explored modifying the connection structures of the prompts. However, the fundamental correlation and distribution between the prompts and image tokens remain unexplored. In this paper, we leverage metric learning techniques to investigate how the distribution of prompts affects fine-tuning performance. Specifically, we propose a novel framework, Distribution Aware Visual Prompt Tuning (DA-VPT), to guide the distributions of the prompts by learning the distance metric from their class-related semantic data. Our method demonstrates that the prompts can serve as an effective bridge to share semantic information between image patches and the class token. We extensively evaluated our approach on popular benchmarks in both recognition and segmentation tasks. The results demonstrate that our approach enables more effective and efficient fine-tuning of ViT models by leveraging semantic information to guide the learning of the prompts, leading to improved performance on various downstream vision tasks. | [
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] |
267 | Towards Lossless Implicit Neural Representation via Bit Plane Decomposition | [
"Woo Kyoung Han",
"Byeonghun Lee",
"Hyunmin Cho",
"Sunghoon Im",
"Kyong Hwan Jin"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Han_Towards_Lossless_Implicit_Neural_Representation_via_Bit_Plane_Decomposition_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Han_Towards_Lossless_Implicit_Neural_Representation_via_Bit_Plane_Decomposition_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_Towards_Lossless_Implicit_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2502.21001 | @InProceedings{Han_2025_CVPR,
author = {Han, Woo Kyoung and Lee, Byeonghun and Cho, Hyunmin and Im, Sunghoon and Jin, Kyong Hwan},
title = {Towards Lossless Implicit Neural Representation via Bit Plane Decomposition},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2269-2278}
} | We quantify the upper bound on the size of the implicit neural representation (INR) model from a digital perspective. The upper bound of the model size increases exponentially as the required bit-precision increases. To this end, we present a bit-plane decomposition method that makes INR predict bit-planes, producing the same effect as reducing the upper bound of the model size. We validate our hypothesis that reducing the upper bound leads to faster convergence with constant model size. Our method achieves lossless representation in 2D image and audio fitting, even for high bit-depth signals, such as 16-bit, which was previously unachievable. We pioneered the presence of bit bias, which INR prioritizes as the most significant bit (MSB). We expand the application of the INR task to bit depth expansion, lossless image compression, and extreme network quantization. Our source code is available at https://github.com/WooKyoungHan/LosslessINR. | [
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268 | Spectral State Space Model for Rotation-Invariant Visual Representation Learning | [
"Sahar Dastani",
"Ali Bahri",
"Moslem Yazdanpanah",
"Mehrdad Noori",
"David Osowiechi",
"Gustavo Adolfo Vargas Hakim",
"Farzad Beizaee",
"Milad Cheraghalikhani",
"Arnab Kumar Mondal",
"Herve Lombaert",
"Christian Desrosiers"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Dastani_Spectral_State_Space_Model_for_Rotation-Invariant_Visual_Representation_Learning_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Dastani_Spectral_State_Space_Model_for_Rotation-Invariant_Visual_Representation_Learning_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dastani_Spectral_State_Space_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.06369 | @InProceedings{Dastani_2025_CVPR,
author = {Dastani, Sahar and Bahri, Ali and Yazdanpanah, Moslem and Noori, Mehrdad and Osowiechi, David and Hakim, Gustavo Adolfo Vargas and Beizaee, Farzad and Cheraghalikhani, Milad and Mondal, Arnab Kumar and Lombaert, Herve and Desrosiers, Christian},
title = {Spectral State Space Model for Rotation-Invariant Visual Representation Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23881-23890}
} | State Space Models (SSMs) have recently emerged as an alternative to Vision Transformers (ViTs) due to their unique ability of modeling global relationships with linear complexity. SSMs are specifically designed to capture spatially proximate relationships of image patches. However, they fail to identify relationships between conceptually related yet not adjacent patches. This limitation arises from the non-causal nature of image data, which lacks inherent directional relationships. Additionally, current vision-based SSMs are highly sensitive to transformations such as rotation. Their predefined scanning directions depend on the original image orientation, which can cause the model to produce inconsistent patch-processing sequences after rotation.To address these limitations, we introduce Spectral VMamba, a novel approach that effectively captures the global structure within an image by leveraging spectral information derived from the graph Laplacian of image patches. Through spectral decomposition, our approach encodes patch relationships independently of image orientation, achieving rotation invariance with the aid of our Rotational Feature Normalizer (RFN) module. Our experiments on classification tasks show that Spectral VMamba outperforms the leading SSM models in vision, such as VMamba, while maintaining invariance to rotations and a providing a similar runtime efficiency. | [
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] |
269 | iSegMan: Interactive Segment-and-Manipulate 3D Gaussians | [
"Yian Zhao",
"Wanshi Xu",
"Ruochong Zheng",
"Pengchong Qiao",
"Chang Liu",
"Jie Chen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_iSegMan_Interactive_Segment-and-Manipulate_3D_Gaussians_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_iSegMan_Interactive_Segment-and-Manipulate_3D_Gaussians_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_iSegMan_Interactive_Segment-and-Manipulate_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2505.11934 | @InProceedings{Zhao_2025_CVPR,
author = {Zhao, Yian and Xu, Wanshi and Zheng, Ruochong and Qiao, Pengchong and Liu, Chang and Chen, Jie},
title = {iSegMan: Interactive Segment-and-Manipulate 3D Gaussians},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {661-670}
} | The efficient rendering and explicit nature of 3DGS promote the advancement of 3D scene manipulation.However, existing methods typically encounter challenges in controlling the manipulation region and are unable to furnish the user with interactive feedback, which inevitably leads to unexpected results.Intuitively, incorporating interactive 3D segmentation tools can compensate for this deficiency. Nevertheless, existing segmentation frameworks impose a pre-processing step of scene-specific parameter training, which limits the efficiency and flexibility of scene manipulation.To deliver a 3D region control module that is well-suited for scene manipulation with reliable efficiency, we propose **i**nteractive **Seg**ment-and-**Man**ipulate 3D Gaussians (**iSegMan**), an interactive segmentation and manipulation framework that only requires simple 2D user interactions in any view.To propagate user interactions to other views, we propose Epipolar-guided Interaction Propagation (**EIP**), which innovatively exploits epipolar constraint for efficient and robust interaction matching.To avoid scene-specific training to maintain efficiency, we further propose the novel Visibility-based Gaussian Voting (**VGV**), which obtains 2D segmentations from SAM and models the region extraction as a voting game between 2D Pixels and 3D Gaussians based on Gaussian visibility.Taking advantage of the efficient and precise region control of EIP and VGV, we put forth a **Manipulation Toolbox** to implement various functions on selected regions, enhancing the controllability, flexibility and practicality of scene manipulation.Extensive results on 3D scene manipulation and segmentation tasks fully demonstrate the significant advantages of iSegMan. | [
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] |
270 | BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices | [
"Xudong Lu",
"Yinghao Chen",
"Cheng Chen",
"Hui Tan",
"Boheng Chen",
"Yina Xie",
"Rui Hu",
"Guanxin Tan",
"Renshou Wu",
"Yan Hu",
"Yi Zeng",
"Lei Wu",
"Liuyang Bian",
"Zhaoxiong Wang",
"Long Liu",
"Yanzhou Yang",
"Han Xiao",
"Aojun Zhou",
"Yafei Wen",
"Xiaoxin Chen",
"Shuai Ren",
"Hongsheng Li"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lu_BlueLM-V-3B_Algorithm_and_System_Co-Design_for_Multimodal_Large_Language_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_BlueLM-V-3B_Algorithm_and_System_Co-Design_for_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_BlueLM-V-3B_Algorithm_and_CVPR_2025_supplemental.pdf | null | @InProceedings{Lu_2025_CVPR,
author = {Lu, Xudong and Chen, Yinghao and Chen, Cheng and Tan, Hui and Chen, Boheng and Xie, Yina and Hu, Rui and Tan, Guanxin and Wu, Renshou and Hu, Yan and Zeng, Yi and Wu, Lei and Bian, Liuyang and Wang, Zhaoxiong and Liu, Long and Yang, Yanzhou and Xiao, Han and Zhou, Aojun and Wen, Yafei and Chen, Xiaoxin and Ren, Shuai and Li, Hongsheng},
title = {BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4145-4155}
} | The emergence and growing popularity of multimodal large language models (MLLMs) have significant potential to enhance various aspects of daily life, from improving communication to facilitating learning and problem-solving. Mobile phones, as essential daily companions, represent the most effective and accessible deployment platform for MLLMs, enabling seamless integration into everyday tasks. However, deploying MLLMs on mobile phones presents challenges due to limitations in memory size and computational capability, making it difficult to achieve smooth and real-time processing without extensive optimization. In this paper, we present BlueLM-V-3B, an algorithm and system co-design approach specifically tailored for the efficient deployment of MLLMs on mobile platforms. To be specific, we redesign the dynamic resolution scheme adopted by mainstream MLLMs and implement system optimization for hardware-aware deployment to optimize model inference on mobile phones. BlueLM-V-3B boasts the following key highlights: (1) Small Size: BlueLM-V-3B features a language model with 2.7B parameters and a vision encoder with 400M parameters. (2) Fast Speed: BlueLM-V-3B achieves a generation speed of 24.4 token/s on the MediaTek Dimensity 9300 processor with 4-bit LLM weight quantization. (3) Strong Performance: BlueLM-V-3B has attained the highest average score of 66.1 on the OpenCompass benchmark among models with <= 4B parameters and surpassed a series of models with much larger parameter sizes (e.g., MiniCPM-V-2.6, InternVL2-8B). | [
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] |
271 | Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization | [
"Peirong Liu",
"Ana Lawry Aguila",
"Juan E. Iglesias"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Unraveling_Normal_Anatomy_via_Fluid-Driven_Anomaly_Randomization_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Unraveling_Normal_Anatomy_via_Fluid-Driven_Anomaly_Randomization_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Unraveling_Normal_Anatomy_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2501.13370 | @InProceedings{Liu_2025_CVPR,
author = {Liu, Peirong and Aguila, Ana Lawry and Iglesias, Juan E.},
title = {Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10455-10465}
} | Data-driven machine learning has made significant strides in medical image analysis. However, most existing methods are tailored to specific modalities and assume a particular resolution (often isotropic). This limits their generalizability in clinical settings, where variations in scan appearance arise from differences in sequence parameters, resolution, and orientation. Furthermore, most general-purpose models are designed for healthy subjects and suffer from performance degradation when pathology is present. We introduce UNA (Unraveling Normal Anatomy), the first modality-agnostic learning approach for normal brain anatomy reconstruction that can handle both healthy scans and cases with pathology. We propose a fluid-driven anomaly randomization method that generates an unlimited number of realistic pathology profiles on-the-fly. UNA is trained on a combination of synthetic and real data, and can be applied directly to real images with potential pathology without the need for fine-tuning. We demonstrate UNA's effectiveness in reconstructing healthy brain anatomy and showcase its direct application to anomaly detection, using both simulated and real images from 3D healthy and stroke datasets, including CT and MRI scans. By bridging the gap between healthy and diseased images, UNA enables the use of general-purpose models on diseased images, opening up new opportunities for large-scale analysis of uncurated clinical images in the presence of pathology. | [
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272 | Taming Teacher Forcing for Masked Autoregressive Video Generation | [
"Deyu Zhou",
"Quan Sun",
"Yuang Peng",
"Kun Yan",
"Runpei Dong",
"Duomin Wang",
"Zheng Ge",
"Nan Duan",
"Xiangyu Zhang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Taming_Teacher_Forcing_for_Masked_Autoregressive_Video_Generation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Taming_Teacher_Forcing_for_Masked_Autoregressive_Video_Generation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Taming_Teacher_Forcing_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2501.12389 | @InProceedings{Zhou_2025_CVPR,
author = {Zhou, Deyu and Sun, Quan and Peng, Yuang and Yan, Kun and Dong, Runpei and Wang, Duomin and Ge, Zheng and Duan, Nan and Zhang, Xiangyu},
title = {Taming Teacher Forcing for Masked Autoregressive Video Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7374-7384}
} | We introduce MAGI, a hybrid video generation framework that combines masked modeling for intra-frame generation with causal modeling for next-frame generation. Our key innovation, Complete Teacher Forcing (CTF), conditions masked frames on complete observation frames rather than masked ones (namely Masked Teacher Forcing, MTF), enabling a smooth transition from token-level (patch-level) to frame-level autoregressive generation. CTF significantly outperforms MTF, achieving a 23% improvement in FVD scores on first-frame conditioned video prediction. To address issues like exposure bias, we employ targeted training strategies, setting a new benchmark in autoregressive video generation. Experiments show that MAGI can generate long, coherent video sequences exceeding 100 frames, even when trained on as few as 16 frames, highlighting its potential for scalable, high-quality video generation. | [
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273 | UniRestore: Unified Perceptual and Task-Oriented Image Restoration Model Using Diffusion Prior | [
"I-Hsiang Chen",
"Wei-Ting Chen",
"Yu-Wei Liu",
"Yuan-Chun Chiang",
"Sy-Yen Kuo",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chen_UniRestore_Unified_Perceptual_and_Task-Oriented_Image_Restoration_Model_Using_Diffusion_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_UniRestore_Unified_Perceptual_and_Task-Oriented_Image_Restoration_Model_Using_Diffusion_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_UniRestore_Unified_Perceptual_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2501.13134 | @InProceedings{Chen_2025_CVPR,
author = {Chen, I-Hsiang and Chen, Wei-Ting and Liu, Yu-Wei and Chiang, Yuan-Chun and Kuo, Sy-Yen and Yang, Ming-Hsuan},
title = {UniRestore: Unified Perceptual and Task-Oriented Image Restoration Model Using Diffusion Prior},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17969-17979}
} | Image restoration aims to recover content from inputs degraded by various factors, such as adverse weather, blur, and noise. Perceptual Image Restoration (PIR) methods improve visual quality but often do not support downstream tasks effectively. On the other hand, Task-oriented Image Restoration (TIR) methods focus on enhancing image utility for high-level vision tasks, sometimes compromising visual quality. This paper introduces UniRestore, a unified image restoration model that bridges the gap between PIR and TIR by using a diffusion prior. The diffusion prior is designed to generate images that align with human visual quality preferences, but these images are often unsuitable for TIR scenarios. To solve this limitation, UniRestore utilizes encoder features from an autoencoder to adapt the diffusion prior to specific tasks. We propose a Complementary Feature Restoration Module (CFRM) to reconstruct degraded encoder features and a Task Feature Adapter (TFA) module to facilitate adaptive feature fusion in the decoder. This design allows UniRestore to optimize images for both human perception and downstream task requirements, addressing discrepancies between visual quality and functional needs. Integrating these modules also enhances UniRestore's adapability and efficiency across diverse tasks. Extensive expertments demonstrate the superior performance of UniRestore in both PIR and TIR scenarios. | [
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274 | Sharp-It: A Multi-view to Multi-view Diffusion Model for 3D Synthesis and Manipulation | [
"Yiftach Edelstein",
"Or Patashnik",
"Dana Cohen-Bar",
"Lihi Zelnik-Manor"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Edelstein_Sharp-It_A_Multi-view_to_Multi-view_Diffusion_Model_for_3D_Synthesis_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Edelstein_Sharp-It_A_Multi-view_to_Multi-view_Diffusion_Model_for_3D_Synthesis_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Edelstein_Sharp-It_A_Multi-view_CVPR_2025_supplemental.zip | null | @InProceedings{Edelstein_2025_CVPR,
author = {Edelstein, Yiftach and Patashnik, Or and Cohen-Bar, Dana and Zelnik-Manor, Lihi},
title = {Sharp-It: A Multi-view to Multi-view Diffusion Model for 3D Synthesis and Manipulation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21458-21468}
} | Advancements in text-to-image diffusion models have led to significant progress in fast 3D content creation. One common approach is to generate a set of multi-view images of an object, and then reconstruct it into a 3D model. However, this approach bypasses the use of a native 3D representation of the object and is hence prone to geometric artifacts and limited in controllability and manipulation capabilities. An alternative approach involves native 3D generative models that directly produce 3D representations. These models, however, are typically limited in their resolution, resulting in lower quality 3D objects. In this work, we bridge the quality gap between methods that directly generate 3D representations and ones that reconstruct 3D objects from multi-view images. We introduce a multi-view to multi-view diffusion model called Sharp-It, which takes a 3D consistent set of multi-view images rendered from a low-quality object and enriches its geometric details and texture. The diffusion model operates on the multi-view set in parallel, in the sense that it shares features across the generated views. A high-quality 3D model can then be reconstructed from the enriched multi-view set. By leveraging the advantages of both 2D and 3D approaches, our method offers an efficient and controllable method for high-quality 3D content creation. We demonstrate that Sharp-It enables various 3D applications, such as fast synthesis, editing, and controlled generation, while attaining high-quality assets. | [
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] |
275 | URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration | [
"Rui Xu",
"Yuzhen Niu",
"Yuezhou Li",
"Huangbiao Xu",
"Wenxi Liu",
"Yuzhong Chen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Xu_URWKV_Unified_RWKV_Model_with_Multi-state_Perspective_for_Low-light_Image_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_URWKV_Unified_RWKV_Model_with_Multi-state_Perspective_for_Low-light_Image_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_URWKV_Unified_RWKV_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2505.23068 | @InProceedings{Xu_2025_CVPR,
author = {Xu, Rui and Niu, Yuzhen and Li, Yuezhou and Xu, Huangbiao and Liu, Wenxi and Chen, Yuzhong},
title = {URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21267-21276}
} | Existing low-light image enhancement (LLIE) and joint LLIE and deblurring (LLIE-deblur) models have made strides in addressing predefined degradations, yet they are often constrained by dynamically coupled degradations. To address these challenges, we introduce a Unified Receptance Weighted Key Value (URWKV) model with multi-state perspective, enabling flexible and effective degradation restoration for low-light images. Specifically, we customize the core URWKV block to perceive and analyze complex degradations by leveraging multiple intra- and inter-stage states. First, inspired by the pupil mechanism in the human visual system, we propose Luminance-adaptive Normalization (LAN) that adjusts normalization parameters based on rich inter-stage states, allowing for adaptive, scene-aware luminance modulation. Second, we aggregate multiple intra-stage states through exponential moving average approach, effectively capturing subtle variations while mitigating information loss inherent in the single-state mechanism. To reduce the degradation effects commonly associated with conventional skip connections, we propose the State-aware Selective Fusion (SSF) module, which dynamically aligns and integrates multi-state features across encoder stages, selectively fusing contextual information. In comparison to state-of-the-art models, our URWKV model achieves superior performance on various benchmarks, while requiring significantly fewer parameters and computational resources. Code is available at: https://github.com/FZU-N/URWKV. | [
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] |
276 | Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift | [
"Siyuan Liang",
"Jiawei Liang",
"Tianyu Pang",
"Chao Du",
"Aishan Liu",
"Mingli Zhu",
"Xiaochun Cao",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Revisiting_Backdoor_Attacks_against_Large_Vision-Language_Models_from_Domain_Shift_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Revisiting_Backdoor_Attacks_against_Large_Vision-Language_Models_from_Domain_Shift_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Revisiting_Backdoor_Attacks_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2406.18844 | @InProceedings{Liang_2025_CVPR,
author = {Liang, Siyuan and Liang, Jiawei and Pang, Tianyu and Du, Chao and Liu, Aishan and Zhu, Mingli and Cao, Xiaochun and Tao, Dacheng},
title = {Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9477-9486}
} | Instruction tuning enhances large vision-language models (LVLMs) but increases their vulnerability to backdoor attacks due to their open design. Unlike prior studies in static settings, this paper explores backdoor attacks in LVLM instruction tuning across mismatched training and testing domains. We introduce a new evaluation dimension, backdoor domain generalization, to assess attack robustness under visual and text domain shifts. Our findings reveal two insights: (1) backdoor generalizability improves when distinctive trigger patterns are independent of specific data domains or model architectures, and (2) the competitive interaction between trigger patterns and clean semantic regions, where guiding the model to predict triggers enhances attack generalizability. Based on these insights, we propose a multimodal attribution backdoor attack (MABA) that injects domain-agnostic triggers into critical areas using attributional interpretation. Experiments with OpenFlamingo, Blip-2, and Otter show that MABA significantly boosts the attack success rate of generalization by 36.4% over the unimodal attack, achieving a 97% success rate at a 0.2% poisoning rate. This study reveals limitations in current evaluations and highlights how enhanced backdoor generalizability poses a security threat to LVLMs, even without test data access. | [
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] |
277 | Condensing Action Segmentation Datasets via Generative Network Inversion | [
"Guodong Ding",
"Rongyu Chen",
"Angela Yao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ding_Condensing_Action_Segmentation_Datasets_via_Generative_Network_Inversion_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ding_Condensing_Action_Segmentation_Datasets_via_Generative_Network_Inversion_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ding_Condensing_Action_Segmentation_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.14112 | @InProceedings{Ding_2025_CVPR,
author = {Ding, Guodong and Chen, Rongyu and Yao, Angela},
title = {Condensing Action Segmentation Datasets via Generative Network Inversion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17733-17742}
} | This work presents the first condensation approach for procedural video datasets used in temporal action segmentation. We propose a condensation framework that leverages generative prior learned from the dataset and network inversion to condense data into compact latent codes with significant storage reduced across temporal and channel aspects. Orthogonally, we propose sampling diverse and representative action sequences to minimize video-wise redundancy. Our evaluation on standard benchmarks demonstrates consistent effectiveness in condensing TAS datasets and achieving competitive performances. Specifically, on the Breakfast dataset, our approach reduces storage by over 500xwhile retaining 83% of the performance compared to training with the full dataset. Furthermore, when applied to a downstream incremental learning task, it yields superior performance compared to the state-of-the-art. | [
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] |
278 | TCFG: Tangential Damping Classifier-free Guidance | [
"Mingi Kwon",
"Shin seong Kim",
"Jaeseok Jeong",
"Yi Ting Hsiao",
"Youngjung Uh"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Kwon_TCFG_Tangential_Damping_Classifier-free_Guidance_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Kwon_TCFG_Tangential_Damping_Classifier-free_Guidance_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kwon_TCFG_Tangential_Damping_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.18137 | @InProceedings{Kwon_2025_CVPR,
author = {Kwon, Mingi and Kim, Shin seong and Jeong, Jaeseok and Hsiao, Yi Ting and Uh, Youngjung},
title = {TCFG: Tangential Damping Classifier-free Guidance},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2620-2629}
} | Diffusion models have achieved remarkable success in text-to-image synthesis, largely attributed to the use of classifier-free guidance (CFG), which enables high-quality, condition-aligned image generation. CFG combines the conditional score (e.g., text-conditioned) with the unconditional score to control the output. However, the unconditional score is in charge of estimating the transition between manifolds of adjacent timesteps from x_t to x_ t-1 , which may inadvertently interfere with the trajectory toward the specific condition. In this work, we introduce a novel approach that leverages a geometric perspective on the unconditional score to enhance CFG performance when conditional scores are available. Specifically, we propose a method that filters the singular vectors of both conditional and unconditional scores using singular value decomposition. This filtering process aligns the unconditional score with the conditional score, thereby refining the sampling trajectory to stay closer to the manifold. Our approach improves image quality with negligible additional computation. We provide deeper insights into the score function behavior in diffusion models and present a practical technique for achieving more accurate and contextually coherent image synthesis. | [
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279 | MatAnyone: Stable Video Matting with Consistent Memory Propagation | [
"Peiqing Yang",
"Shangchen Zhou",
"Jixin Zhao",
"Qingyi Tao",
"Chen Change Loy"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yang_MatAnyone_Stable_Video_Matting_with_Consistent_Memory_Propagation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_MatAnyone_Stable_Video_Matting_with_Consistent_Memory_Propagation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_MatAnyone_Stable_Video_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2501.14677 | @InProceedings{Yang_2025_CVPR,
author = {Yang, Peiqing and Zhou, Shangchen and Zhao, Jixin and Tao, Qingyi and Loy, Chen Change},
title = {MatAnyone: Stable Video Matting with Consistent Memory Propagation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7299-7308}
} | Auxiliary-free human video matting methods, which rely solely on input frames, often struggle with complex or ambiguous backgrounds. To tackle this, we propose MatAnyone, a practical framework designed for target-assigned video matting. Specifically, building on a memory-based framework, we introduce a consistent memory propagation module via region-adaptive memory fusion, which adaptively combines memory from the previous frame. This ensures stable semantic consistency in core regions while maintaining fine details along object boundaries. For robust training, we present a larger, high-quality, and diverse dataset for video matting. Additionally, we incorporate a novel training strategy that efficiently leverages large-scale segmentation data, further improving matting stability. With this new network design, dataset, and training strategy, MatAnyone delivers robust, accurate video matting in diverse real-world scenarios, outperforming existing methods. The code and model will be publicly available. | [
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] |
280 | Can Generative Video Models Help Pose Estimation? | [
"Ruojin Cai",
"Jason Y. Zhang",
"Philipp Henzler",
"Zhengqi Li",
"Noah Snavely",
"Ricardo Martin-Brualla"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Cai_Can_Generative_Video_Models_Help_Pose_Estimation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_Can_Generative_Video_Models_Help_Pose_Estimation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cai_Can_Generative_Video_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.16155 | @InProceedings{Cai_2025_CVPR,
author = {Cai, Ruojin and Zhang, Jason Y. and Henzler, Philipp and Li, Zhengqi and Snavely, Noah and Martin-Brualla, Ricardo},
title = {Can Generative Video Models Help Pose Estimation?},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16764-16773}
} | Pairwise pose estimation from images with little or no overlap is an open challenge in computer vision. Existing methods, even those trained on large-scale datasets, struggle in these scenarios due to the lack of identifiable correspondences or visual overlap. Inspired by the human ability to infer spatial relationships from diverse scenes, we propose a novel approach, InterPose, that leverages the rich priors encoded within pre-trained generative video models. We propose to use a video model to hallucinate intermediate frames between two input images, effectively creating a dense, visual transition, which significantly simplifies the problem of pose estimation. Since current video models can still produce implausible motion or inconsistent geometry, we introduce a self-consistency score that evaluates the consistency of pose predictions from sampled videos. We demonstrate that our approach generalizes among three state-of-the-art video models and show consistent improvements over the state-of-the-art DUSt3R baseline on four diverse datasets encompassing indoor, outdoor, and object-centric scenes. Our findings suggest a promising avenue for improving pose estimation models by leveraging large generative models trained on vast amounts of video data, which is more readily available than 3D data. See our project page for results: Inter-Pose.github.io. | [
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] |
281 | Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models | [
"Matt Deitke",
"Christopher Clark",
"Sangho Lee",
"Rohun Tripathi",
"Yue Yang",
"Jae Sung Park",
"Mohammadreza Salehi",
"Niklas Muennighoff",
"Kyle Lo",
"Luca Soldaini",
"Jiasen Lu",
"Taira Anderson",
"Erin Bransom",
"Kiana Ehsani",
"Huong Ngo",
"YenSung Chen",
"Ajay Patel",
"Mark Yatskar",
"Chris Callison-Burch",
"Andrew Head",
"Rose Hendrix",
"Favyen Bastani",
"Eli VanderBilt",
"Nathan Lambert",
"Yvonne Chou",
"Arnavi Chheda",
"Jenna Sparks",
"Sam Skjonsberg",
"Michael Schmitz",
"Aaron Sarnat",
"Byron Bischoff",
"Pete Walsh",
"Chris Newell",
"Piper Wolters",
"Tanmay Gupta",
"Kuo-Hao Zeng",
"Jon Borchardt",
"Dirk Groeneveld",
"Crystal Nam",
"Sophie Lebrecht",
"Caitlin Wittlif",
"Carissa Schoenick",
"Oscar Michel",
"Ranjay Krishna",
"Luca Weihs",
"Noah A. Smith",
"Hannaneh Hajishirzi",
"Ross Girshick",
"Ali Farhadi",
"Aniruddha Kembhavi"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Deitke_Molmo_and_PixMo_Open_Weights_and_Open_Data_for_State-of-the-Art_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Deitke_Molmo_and_PixMo_Open_Weights_and_Open_Data_for_State-of-the-Art_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deitke_Molmo_and_PixMo_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2409.17146 | @InProceedings{Deitke_2025_CVPR,
author = {Deitke, Matt and Clark, Christopher and Lee, Sangho and Tripathi, Rohun and Yang, Yue and Park, Jae Sung and Salehi, Mohammadreza and Muennighoff, Niklas and Lo, Kyle and Soldaini, Luca and Lu, Jiasen and Anderson, Taira and Bransom, Erin and Ehsani, Kiana and Ngo, Huong and Chen, YenSung and Patel, Ajay and Yatskar, Mark and Callison-Burch, Chris and Head, Andrew and Hendrix, Rose and Bastani, Favyen and VanderBilt, Eli and Lambert, Nathan and Chou, Yvonne and Chheda, Arnavi and Sparks, Jenna and Skjonsberg, Sam and Schmitz, Michael and Sarnat, Aaron and Bischoff, Byron and Walsh, Pete and Newell, Chris and Wolters, Piper and Gupta, Tanmay and Zeng, Kuo-Hao and Borchardt, Jon and Groeneveld, Dirk and Nam, Crystal and Lebrecht, Sophie and Wittlif, Caitlin and Schoenick, Carissa and Michel, Oscar and Krishna, Ranjay and Weihs, Luca and Smith, Noah A. and Hajishirzi, Hannaneh and Girshick, Ross and Farhadi, Ali and Kembhavi, Aniruddha},
title = {Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {91-104}
} | Today's most advanced vision-language models (VLMs) remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good performance, effectively distilling these closed VLMs into open ones. As a result, the community has been missing foundational knowledge about how to build performant VLMs from scratch. We present Molmo, a new family of VLMs that are state-of-the-art in their class of openness. Our key contribution is a collection of new datasets called PixMo, including a dataset of highly detailed image captions for pre-training, a free-form image Q&A dataset for fine-tuning, and an innovative 2D pointing dataset, all collected without the use of external VLMs. The success of our approach relies on careful modeling choices, a well-tuned training pipeline, and, most critically, the quality of our newly collected datasets. Our best-in-class 72B model not only outperforms others in the class of open weight and data models, but also outperforms larger proprietary models including Claude 3.5 Sonnet, and Gemini 1.5 Pro and Flash, second only to GPT-4o based on both academic benchmarks and on a large human evaluation. Our model weights, new datasets, and source code are available at https://molmo.allenai.org/blog. | [
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282 | DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving | [
"Zhenhua Xu",
"Yan Bai",
"Yujia Zhang",
"Zhuoling Li",
"Fei Xia",
"Kwan-Yee K. Wong",
"Jianqiang Wang",
"Hengshuang Zhao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Xu_DriveGPT4-V2_Harnessing_Large_Language_Model_Capabilities_for_Enhanced_Closed-Loop_Autonomous_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_DriveGPT4-V2_Harnessing_Large_Language_Model_Capabilities_for_Enhanced_Closed-Loop_Autonomous_CVPR_2025_paper.pdf | null | null | @InProceedings{Xu_2025_CVPR,
author = {Xu, Zhenhua and Bai, Yan and Zhang, Yujia and Li, Zhuoling and Xia, Fei and Wong, Kwan-Yee K. and Wang, Jianqiang and Zhao, Hengshuang},
title = {DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17261-17270}
} | Multimodal large language models (MLLMs) possess the ability to comprehend visual images or videos, and show impressive reasoning ability thanks to the vast amounts of pretrained knowledge, making them highly suitable for autonomous driving applications. Unlike the previous work, DriveGPT4-V1, which focused on open-loop tasks, this study explores the capabilities of LLMs in enhancing closed-loop autonomous driving. DriveGPT4-V2 processes camera images and vehicle states as input to generate low-level control signals for end-to-end vehicle operation. A multi-view visual tokenizer (MV-VT) is employed enabling DriveGPT4-V2 to perceive the environment with an extensive range while maintaining critical details. The model architecture has been refined to improve decision prediction and inference speed. To further enhance the performance, an additional expert LLM is trained for online imitation learning. The expert LLM, sharing a similar structure with DriveGPT4-V2, can access privileged information about surrounding objects for more robust and reliable predictions. Experimental results show that DriveGPT4-V2 outperforms all baselines on the challenging CARLA Longest6 benchmark. The code and data of DriveGPT4-V2 will be publicly available. | [
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] |
283 | High-Fidelity Lightweight Mesh Reconstruction from Point Clouds | [
"Chen Zhang",
"Wentao Wang",
"Ximeng Li",
"Xinyao Liao",
"Wanjuan Su",
"Wenbing Tao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_High-Fidelity_Lightweight_Mesh_Reconstruction_from_Point_Clouds_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_High-Fidelity_Lightweight_Mesh_Reconstruction_from_Point_Clouds_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_High-Fidelity_Lightweight_Mesh_CVPR_2025_supplemental.zip | null | @InProceedings{Zhang_2025_CVPR,
author = {Zhang, Chen and Wang, Wentao and Li, Ximeng and Liao, Xinyao and Su, Wanjuan and Tao, Wenbing},
title = {High-Fidelity Lightweight Mesh Reconstruction from Point Clouds},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11739-11748}
} | Recently, learning signed distance functions (SDFs) from point clouds has become popular for reconstruction. To ensure accuracy, most methods require using high-resolution Marching Cubes for surface extraction. However, this results in redundant mesh elements, making the mesh inconvenient to use. To solve the problem, we propose an adaptive meshing method to extract resolution-adaptive meshes based on surface curvature, enabling the recovery of high-fidelity lightweight meshes. Specifically, we first use point-based representation to perceive implicit surfaces and calculate surface curvature. A vertex generator is designed to produce curvature-adaptive vertices with any specified number on the implicit surface, preserving the overall structure and high-curvature features. Then we develop a Delaunay meshing algorithm to generate meshes from vertices, ensuring geometric fidelity and correct topology. In addition, to obtain accurate SDFs for adaptive meshing and achieve better lightweight reconstruction, we design a hybrid representation combining feature grid and feature tri-plane for better detail capture. Experiments demonstrate that our method can generate high-quality lightweight meshes from point clouds. Compared with methods from various categories, our approach achieves superior results, especially in capturing more details with fewer elements. | [
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284 | MDP: Multidimensional Vision Model Pruning with Latency Constraint | [
"Xinglong Sun",
"Barath Lakshmanan",
"Maying Shen",
"Shiyi Lan",
"Jingde Chen",
"Jose M. Alvarez"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Sun_MDP_Multidimensional_Vision_Model_Pruning_with_Latency_Constraint_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_MDP_Multidimensional_Vision_Model_Pruning_with_Latency_Constraint_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_MDP_Multidimensional_Vision_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2504.02168 | @InProceedings{Sun_2025_CVPR,
author = {Sun, Xinglong and Lakshmanan, Barath and Shen, Maying and Lan, Shiyi and Chen, Jingde and Alvarez, Jose M.},
title = {MDP: Multidimensional Vision Model Pruning with Latency Constraint},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20113-20123}
} | Current structural pruning methods face two significant limitations: (i) they often limit pruning to finer-grained levels like channels, making aggressive parameter reduction challenging, and (ii) they focus heavily on parameter and FLOP reduction, with existing latency-aware methods frequently relying on simplistic, suboptimal linear models that fail to generalize well to transformers, where multiple interacting dimensions impact latency. In this paper, we address both limitations by introducing Multi-Dimensional Pruning(MDP), a novel paradigm that jointly optimizes across a variety of pruning granularities--including channels, query/key, heads, embeddings, and blocks. MDP employs an advanced latency modeling technique to accurately capture latency variations across all prunable dimensions, achieving an optimal balance between latency and accuracy. By reformulating pruning as a Mixed-Integer Nonlinear Program (MINLP), MDP efficiently identifies the optimal pruned structure across all prunable dimensions while respecting latency constraints. This versatile framework supports both CNNs and transformers. Extensive experiments demonstrate that MDP significantly outperforms previous methods, especially at high pruning ratios. On ImageNet, MDP achieves a 28% speed increase with a +1.4 Top-1 accuracy improvement over prior work like HALP for ResNet50 pruning. Against the latest transformer pruning method, Isomorphic, MDP delivers an additional 37% acceleration with a +0.7 Top-1 accuracy improvement. | [
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] |
285 | OSDFace: One-Step Diffusion Model for Face Restoration | [
"Jingkai Wang",
"Jue Gong",
"Lin Zhang",
"Zheng Chen",
"Xing Liu",
"Hong Gu",
"Yutong Liu",
"Yulun Zhang",
"Xiaokang Yang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_OSDFace_One-Step_Diffusion_Model_for_Face_Restoration_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_OSDFace_One-Step_Diffusion_Model_for_Face_Restoration_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_OSDFace_One-Step_Diffusion_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.17163 | @InProceedings{Wang_2025_CVPR,
author = {Wang, Jingkai and Gong, Jue and Zhang, Lin and Chen, Zheng and Liu, Xing and Gu, Hong and Liu, Yutong and Zhang, Yulun and Yang, Xiaokang},
title = {OSDFace: One-Step Diffusion Model for Face Restoration},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12626-12636}
} | Diffusion models have demonstrated impressive performance in face restoration. Yet, their multi-step inference process remains computationally intensive, limiting their applicability in real-world scenarios. Moreover, existing methods often struggle to generate face images that are harmonious, realistic, and consistent with the subject's identity. In this work, we propose OSDFace, a novel one-step diffusion model for face restoration. Specifically, we propose a visual representation embedder (VRE) to better capture prior information and understand the input face. In VRE, low-quality faces are processed by a visual tokenizer and subsequently embedded with a vector-quantized dictionary to generate visual prompts. Additionally, we incorporate a facial identity loss derived from face recognition to further ensure identity consistency. We further employ a generative adversarial network (GAN) as a guidance model to encourage distribution alignment between the restored face and the ground truth. Experimental results demonstrate that OSDFace surpasses current state-of-the-art (SOTA) methods in both visual quality and quantitative metrics, generating high-fidelity, natural face images with high identity consistency. The code and model will be released at https://github.com/jkwang28/OSDFace. | [
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] |
286 | Task Singular Vectors: Reducing Task Interference in Model Merging | [
"Antonio Andrea Gargiulo",
"Donato Crisostomi",
"Maria Sofia Bucarelli",
"Simone Scardapane",
"Fabrizio Silvestri",
"Emanuele Rodolà"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Gargiulo_Task_Singular_Vectors_Reducing_Task_Interference_in_Model_Merging_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Gargiulo_Task_Singular_Vectors_Reducing_Task_Interference_in_Model_Merging_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gargiulo_Task_Singular_Vectors_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.00081 | @InProceedings{Gargiulo_2025_CVPR,
author = {Gargiulo, Antonio Andrea and Crisostomi, Donato and Bucarelli, Maria Sofia and Scardapane, Simone and Silvestri, Fabrizio and Rodol\`a, Emanuele},
title = {Task Singular Vectors: Reducing Task Interference in Model Merging},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18695-18705}
} | Task Arithmetic has emerged as a simple yet effective method to merge models without additional training. However, by treating entire networks as flat parameter vectors, it overlooks key structural information and is susceptible to task interference. In this paper, we study task vectors at the layer level, focusing on task layer matrices and their singular value decomposition. In particular, we concentrate on the resulting singular vectors, which we refer to as Task Singular Vectors (TSV). Recognizing that layer task matrices are often low-rank, we propose TSV-Compress, a simple procedure that compresses them to 10% of their original size while retaining 99% of accuracy. We further leverage this low-rank space to define a new measure of task interference based on the interaction of singular vectors from different tasks. Building on these findings, we introduce TSV-Merge, a novel model merging approach that combines compression with interference reduction, significantly outperforming existing methods. | [
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] |
287 | Functionality Understanding and Segmentation in 3D Scenes | [
"Jaime Corsetti",
"Francesco Giuliari",
"Alice Fasoli",
"Davide Boscaini",
"Fabio Poiesi"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Corsetti_Functionality_Understanding_and_Segmentation_in_3D_Scenes_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Corsetti_Functionality_Understanding_and_Segmentation_in_3D_Scenes_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Corsetti_Functionality_Understanding_and_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.16310 | @InProceedings{Corsetti_2025_CVPR,
author = {Corsetti, Jaime and Giuliari, Francesco and Fasoli, Alice and Boscaini, Davide and Poiesi, Fabio},
title = {Functionality Understanding and Segmentation in 3D Scenes},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24550-24559}
} | Understanding functionalities in 3D scenes involves interpreting natural language descriptions to locate functional interactive objects, such as handles and buttons, in a 3D environment. Functionality understanding is highly challenging, as it requires both world knowledge to interpret language and spatial perception to identify fine-grained objects. For example, given a task like "turn on the ceiling light", an embodied AI agent must infer that it needs to locate the light switch, even though the switch is not explicitly mentioned in the task description.To date, no dedicated methods have been developed for this problem. In this paper, we introduce Fun3DU, the first approach designed for functionality understanding in 3D scenes. Fun3DU uses a language model to parse the task description through Chain-of-Thought reasoning in order to identify the object of interest. The identified object is segmented across multiple views of the captured scene by using a VLM. The segmentation results from each view are lifted in 3D and aggregated into the point cloud using geometric information. Fun3DU is training-free, relying entirely on pre-trained models. We evaluate Fun3DU on SceneFun3D, the most recent and only dataset to benchmark this task, which comprises over 3000 task descriptions on 230 scenes. Our method significantly outperforms state-of-the-art open-vocabulary 3D segmentation approaches. Project page: https://tev-fbk.github.io/fun3du | [
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288 | Dragin3D: Image Editing by Dragging in 3D Space | [
"Weiran Guang",
"Xiaoguang Gu",
"Mengqi Huang",
"Zhendong Mao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Guang_Dragin3D_Image_Editing_by_Dragging_in_3D_Space_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Guang_Dragin3D_Image_Editing_by_Dragging_in_3D_Space_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guang_Dragin3D_Image_Editing_CVPR_2025_supplemental.pdf | null | @InProceedings{Guang_2025_CVPR,
author = {Guang, Weiran and Gu, Xiaoguang and Huang, Mengqi and Mao, Zhendong},
title = {Dragin3D: Image Editing by Dragging in 3D Space},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21502-21512}
} | Interactive drag editing of images is a valuable task that has gained considerable attention for its precision and controllability. However, existing approaches have primarily focused on manipulating the shape or movement of objects in 2D plane. We propose to extend this drag-based editing task to 3D space. Firstly, we utilize the trajectory of two points to represent the rotational trajectory of the object. Gaussian maps of a circle and a square are centered at these two points, respectively. We use distinct shapes to ensure that symmetric views produce different object representations. Secondly, we introduce a lightweight mapping network to embed the object features into two Gaussian maps to obtain a continuous control condition that guides the model in learning the correspondence between the trajectory and the object. Finally, to overcome the limitations of current 3D object reconstruction datasets, which typically consist of object maps with transparent backgrounds, we affix random backgrounds to them. This modification helps improve the model's ability to ignore background interference when editing real images with complex backgrounds. Experiments demonstrate that our approach successfully achieves object rotation within the drag framework and demonstrates strong generalization to real-world images. | [
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289 | MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception | [
"Wenzhuo Liu",
"Wenshuo Wang",
"Yicheng Qiao",
"Qiannan Guo",
"Jiayin Zhu",
"Pengfei Li",
"Zilong Chen",
"Huiming Yang",
"Zhiwei Li",
"Lening Wang",
"Tiao Tan",
"Huaping Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MMTL-UniAD_A_Unified_Framework_for_Multimodal_and_Multi-Task_Learning_in_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MMTL-UniAD_A_Unified_Framework_for_Multimodal_and_Multi-Task_Learning_in_CVPR_2025_paper.pdf | null | null | @InProceedings{Liu_2025_CVPR,
author = {Liu, Wenzhuo and Wang, Wenshuo and Qiao, Yicheng and Guo, Qiannan and Zhu, Jiayin and Li, Pengfei and Chen, Zilong and Yang, Huiming and Li, Zhiwei and Wang, Lening and Tan, Tiao and Liu, Huaping},
title = {MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6864-6874}
} | Advanced driver assistance systems require a comprehensive understanding of the driver's mental/physical state and traffic context but existing works often neglect the potential benefits of joint learning between these tasks. This paper proposes MMTL-UniAD, a unified multi-modal multi-task learning framework that simultaneously recognizes driver behavior (e.g., looking around, talking), driver emotion (e.g., anxiety, happiness), vehicle behavior (e.g., parking, turning), and traffic context (e.g., traffic jam, traffic smooth). A key challenge is avoiding negative transfer between tasks, which can impair learning performance. To address this, we introduce two key components into the framework: one is the multi-axis region attention network to extract global context-sensitive features, and the other is the dual-branch multimodal embedding to learn multimodal embeddings from both task-shared and task-specific features. The former uses a multi-attention mechanism to extract task-relevant features, mitigating negative transfer caused by task-unrelated features. The latter employs a dual-branch structure to adaptively adjust task-shared and task-specific parameters, enhancing cross-task knowledge transfer while reducing task conflicts. We assess MMTL-UniAD on the AIDE dataset, using a series of ablation studies, and show that it outperforms state-of-the-art methods across all four tasks. The code is available on https://github.com/Wenzhuo-Liu/MMTL-UniAD. | [
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290 | T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation | [
"Kaiyue Sun",
"Kaiyi Huang",
"Xian Liu",
"Yue Wu",
"Zihan Xu",
"Zhenguo Li",
"Xihui Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Sun_T2V-CompBench_A_Comprehensive_Benchmark_for_Compositional_Text-to-video_Generation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_T2V-CompBench_A_Comprehensive_Benchmark_for_Compositional_Text-to-video_Generation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_T2V-CompBench_A_Comprehensive_CVPR_2025_supplemental.pdf | null | @InProceedings{Sun_2025_CVPR,
author = {Sun, Kaiyue and Huang, Kaiyi and Liu, Xian and Wu, Yue and Xu, Zihan and Li, Zhenguo and Liu, Xihui},
title = {T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8406-8416}
} | Text-to-video (T2V) generative models have advanced significantly, yet their ability to compose different objects, attributes, actions, and motions into a video remains unexplored. Previous text-to-video benchmarks also neglect this important ability for evaluation. In this work, we conduct the first systematic study on compositional text-to-video generation. We propose T2V-CompBench, the first benchmark tailored for compositional text-to-video generation. T2V-CompBench encompasses diverse aspects of compositionality, including consistent attribute binding, dynamic attribute binding, spatial relationships, motion binding, action binding, object interactions, and generative numeracy. We further carefully design evaluation metrics of multimodal large language model (MLLM)-based, detection-based, and tracking-based metrics, which can better reflect the compositional text-to-video generation quality of seven proposed categories with 1400 text prompts. The effectiveness of the proposed metrics is verified by correlation with human evaluations. We also benchmark various text-to-video generative models and conduct in-depth analysis across different models and various compositional categories. We find that compositional text-to-video generation is highly challenging for current models, and we hope our attempt could shed light on future research in this direction. | [
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] |
291 | Self-Evolving Visual Concept Library using Vision-Language Critics | [
"Atharva Sehgal",
"Patrick Yuan",
"Ziniu Hu",
"Yisong Yue",
"Jennifer J. Sun",
"Swarat Chaudhuri"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Sehgal_Self-Evolving_Visual_Concept_Library_using_Vision-Language_Critics_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Sehgal_Self-Evolving_Visual_Concept_Library_using_Vision-Language_Critics_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sehgal_Self-Evolving_Visual_Concept_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2504.00185 | @InProceedings{Sehgal_2025_CVPR,
author = {Sehgal, Atharva and Yuan, Patrick and Hu, Ziniu and Yue, Yisong and Sun, Jennifer J. and Chaudhuri, Swarat},
title = {Self-Evolving Visual Concept Library using Vision-Language Critics},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13124-13134}
} | We study the problem of building a visual concept library for visual recognition. Building effective visual concept libraries is challenging, as manual definition is labor-intensive, while relying solely on LLMs for concept generation can result in concepts that lack discriminative power or fail to account for the complex interactions between them. Our approach, ESCHER, takes a library learning perspective to iteratively discover and improve visual concepts. ESCHER uses a vision-language model (VLM) as a critic to iteratively refine the concept library, including accounting for interactions between concepts and how they affect downstream classifiers. By leveraging the in-context learning abilities of LLMs and the history of performance using various concepts, ESCHER dynamically improves its concept generation strategy based on the VLM critic's feedback. Finally, ESCHER does not require any human annotations, and is thus an automated plug-and-play framework. We empirically demonstrate the ability of ESCHER to learn a concept library for zero-shot, few-shot, and fine-tuning visual classification tasks. This work represents, to our knowledge, the first application of concept library learning to real-world visual tasks. | [
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] |
292 | Multimodal Autoregressive Pre-training of Large Vision Encoders | [
"Enrico Fini",
"Mustafa Shukor",
"Xiujun Li",
"Philipp Dufter",
"Michal Klein",
"David Haldimann",
"Sai Aitharaju",
"Victor G. Turrisi da Costa",
"Louis Béthune",
"Zhe Gan",
"Alexander Toshev",
"Marcin Eichner",
"Moin Nabi",
"Yinfei Yang",
"Joshua Susskind",
"Alaaeldin El-Nouby"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Fini_Multimodal_Autoregressive_Pre-training_of_Large_Vision_Encoders_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Fini_Multimodal_Autoregressive_Pre-training_of_Large_Vision_Encoders_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fini_Multimodal_Autoregressive_Pre-training_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.14402 | @InProceedings{Fini_2025_CVPR,
author = {Fini, Enrico and Shukor, Mustafa and Li, Xiujun and Dufter, Philipp and Klein, Michal and Haldimann, David and Aitharaju, Sai and da Costa, Victor G. Turrisi and B\'ethune, Louis and Gan, Zhe and Toshev, Alexander and Eichner, Marcin and Nabi, Moin and Yang, Yinfei and Susskind, Joshua and El-Nouby, Alaaeldin},
title = {Multimodal Autoregressive Pre-training of Large Vision Encoders},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9641-9654}
} | We introduce a novel method for pre-training of large-scale vision encoders. Building on recent advancements in autoregressive pre-training of vision models, we extend this framework to a multimodal setting, i.e., images and text. In this paper, we present AIMV2, a family of generalist vision encoders characterized by a straightforward pre-training process, scalability, and remarkable performance across a range of downstream tasks. This is achieved by pairing the vision encoder with a multimodal decoder that autoregressively generates raw image patches and text tokens. Our encoders excel not only in multimodal evaluations but also in vision benchmarks such as localization, grounding, and classification. Notably, our AIMV2-3B encoder achieves 89.5% accuracy on ImageNet-1k with a frozen trunk. Fur- thermore, AIMV2 consistently outperforms state-of-the-art contrastive models (e.g., CLIP, SigLIP) in multimodal im- age understanding across diverse settings. | [
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] |
293 | AKiRa: Augmentation Kit on Rays for Optical Video Generation | [
"Xi Wang",
"Robin Courant",
"Marc Christie",
"Vicky Kalogeiton"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_AKiRa_Augmentation_Kit_on_Rays_for_Optical_Video_Generation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_AKiRa_Augmentation_Kit_on_Rays_for_Optical_Video_Generation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_AKiRa_Augmentation_Kit_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.14158 | @InProceedings{Wang_2025_CVPR,
author = {Wang, Xi and Courant, Robin and Christie, Marc and Kalogeiton, Vicky},
title = {AKiRa: Augmentation Kit on Rays for Optical Video Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2609-2619}
} | Recent advances in text-conditioned video diffusion have greatly improved video quality. However, these methods offer limited or sometimes no control to users on camera aspects, including dynamic camera motion, zoom, distorted lens and focus shifts. These motion and optical aspects are crucial for adding controllability and cinematic elements to generation frameworks, ultimately resulting in visual content that draws focus, enhances mood, and guides emotions according to filmmakers' controls. In this paper, we aim to close the gap between controllable video generation and camera optics. To achieve this, we propose AKiRa (Augmentation Kit on Rays), a novel augmentation framework that builds and trains a camera adapter with a complex camera model over an existing video generation backbone. It enables fine-tuned control over camera motion as well as complex optical parameters (focal length, distortion, aperture) to achieve cinematic effects such as zoom, fisheye effect, and bokeh. Extensive experiments demonstrate AKiRa's effectiveness in combining and composing camera optics while outperforming all state-of-the-art methods. This work sets a new landmark in controlled and optically enhanced video generation, paving the way for future camera diffusion methods. | [
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294 | Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory | [
"Wenliang Zhong",
"Haoyu Tang",
"Qinghai Zheng",
"Mingzhu Xu",
"Yupeng Hu",
"Weili Guan"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_Towards_Stable_and_Storage-efficient_Dataset_Distillation_Matching_Convexified_Trajectory_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhong_Towards_Stable_and_Storage-efficient_Dataset_Distillation_Matching_Convexified_Trajectory_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhong_Towards_Stable_and_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2406.19827 | @InProceedings{Zhong_2025_CVPR,
author = {Zhong, Wenliang and Tang, Haoyu and Zheng, Qinghai and Xu, Mingzhu and Hu, Yupeng and Guan, Weili},
title = {Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25581-25589}
} | The rapid evolution of deep learning and large language models has led to an exponential growth in the demand for training data, prompting the development of Dataset Distillation methods to address the challenges of managing large datasets. Among these, Matching Training Trajectories (MTT) has been a prominent approach, which replicates the training trajectory of an expert network on real data with a synthetic dataset. However, our investigation found that this method suffers from three significant limitations: 1. Instability of expert trajectory generated by Stochastic Gradient Descent (SGD); 2. Low convergence speed of the distillation process; 3. High storage consumption of the expert trajectory. To address these issues, we offer a new perspective on understanding the essence of Dataset Distillation and MTT through a simple transformation of the objective function, and introduce a novel method called Matching Convexified Trajectory (MCT), which aims to provide better guidance for the student trajectory. MCT creates convex combinations of expert trajectories by selecting a few expert models, guiding student networks to converge quickly and stably. This trajectory is not only easier to store, but also enables continuous sampling strategies during the distillation process, ensuring thorough learning and fitting of the entire expert trajectory. The comprehensive experiment of three public datasets verified that MCT is superior to the traditional MTT method. | [
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295 | TSAM: Temporal SAM Augmented with Multimodal Prompts for Referring Audio-Visual Segmentation | [
"Abduljalil Radman",
"Jorma Laaksonen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Radman_TSAM_Temporal_SAM_Augmented_with_Multimodal_Prompts_for_Referring_Audio-Visual_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Radman_TSAM_Temporal_SAM_Augmented_with_Multimodal_Prompts_for_Referring_Audio-Visual_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Radman_TSAM_Temporal_SAM_CVPR_2025_supplemental.pdf | null | @InProceedings{Radman_2025_CVPR,
author = {Radman, Abduljalil and Laaksonen, Jorma},
title = {TSAM: Temporal SAM Augmented with Multimodal Prompts for Referring Audio-Visual Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23947-23956}
} | Referring audio-visual segmentation (Ref-AVS) aims to segment objects within audio-visual scenes using multimodal cues embedded in text expressions. While the Segment Anything Model (SAM) has revolutionized visual segmentation, its applicability to Ref-AVS, where multimodal cues act as novel prompts, remains unexplored. SAM's limitation to single-frame segmentation also hinders its ability to capture essential temporal context needed for multi-frame audio-visual segmentation. To address this gap, we propose TSAM, a novel extension of SAM designed to leverage multimodal cues for precise segmentation in dynamic audio-visual scenes. TSAM enhances SAM's image encoder with a temporal modeling branch, enabling spatio-temporal learning and deep multimodal fusion across video frames, while retaining SAM's pre-trained knowledge. Additionally, TSAM replaces SAM's user-interactive prompting mechanism with sparse and dense data-driven prompts, enabling more effective integration of audio-visual inputs and reference text expressions. Extensive experiments on the Ref-AVS dataset demonstrate TSAM's superiority over state-of-the-art methods. The results illustrate its effectiveness in segmenting objects in dynamic audio-visual scenes using text-based multimodal cues and its strong generalization to unseen objects. | [
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296 | TFCustom: Customized Image Generation with Time-Aware Frequency Feature Guidance | [
"Mushui Liu",
"Dong She",
"Jingxuan Pang",
"Qihan Huang",
"Jiacheng Ying",
"Wanggui He",
"Yuanlei Hou",
"Siming Fu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_TFCustom_Customized_Image_Generation_with_Time-Aware_Frequency_Feature_Guidance_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_TFCustom_Customized_Image_Generation_with_Time-Aware_Frequency_Feature_Guidance_CVPR_2025_paper.pdf | null | null | @InProceedings{Liu_2025_CVPR,
author = {Liu, Mushui and She, Dong and Pang, Jingxuan and Huang, Qihan and Ying, Jiacheng and He, Wanggui and Hou, Yuanlei and Fu, Siming},
title = {TFCustom: Customized Image Generation with Time-Aware Frequency Feature Guidance},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2714-2723}
} | Subject-driven image personalization has seen notable advancements, especially with the advent of the ReferenceNet paradigm. ReferenceNet excels in integrating image reference features, making it highly applicable in creative and commercial settings. However, current implementations of ReferenceNet primarily operate as latent-level feature extractors, which limit their potential. This constraint hinders the provision of appropriate features to the denoising backbone across different timesteps, leading to suboptimal image consistency. In this paper, we revisit the extraction of reference features and propose TFCustom, a model framework designed to focus on reference image features at different temporal steps and frequency levels. Specifically, we firstly propose synchronized ReferenceNet to extract reference image features while simultaneously optimizing noise injection and denoising for the reference image. We also propose a time-aware frequency feature refinement module that leverages high- and low-frequency filters, combined with time embeddings, to adaptively select the degree of reference feature injection. Additionally, to enhance the similarity between reference objects and the generated image, we introduce a novel reward-based loss that encourages greater alignment between the reference and generated images. Experimental results demonstrate state-of-the-art performance in both multi-object and single-object reference generation, with significant improvements in texture and textual detail generation over existing methods. | [
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297 | Boosting Point-Supervised Temporal Action Localization through Integrating Query Reformation and Optimal Transport | [
"Mengnan Liu",
"Le Wang",
"Sanping Zhou",
"Kun Xia",
"Xiaolong Sun",
"Gang Hua"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Boosting_Point-Supervised_Temporal_Action_Localization_through_Integrating_Query_Reformation_and_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Boosting_Point-Supervised_Temporal_Action_Localization_through_Integrating_Query_Reformation_and_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Boosting_Point-Supervised_Temporal_CVPR_2025_supplemental.pdf | null | @InProceedings{Liu_2025_CVPR,
author = {Liu, Mengnan and Wang, Le and Zhou, Sanping and Xia, Kun and Sun, Xiaolong and Hua, Gang},
title = {Boosting Point-Supervised Temporal Action Localization through Integrating Query Reformation and Optimal Transport},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13865-13875}
} | Point-supervised Temporal Action Localization poses significant challenges due to the difficulty of identifying complete actions with a single-point annotation per action. Existing methods typically employ Multiple Instance Learning, which struggles to capture global temporal context and requires heuristic post-processing. In research on fully-supervised tasks, DETR-based structures have effectively addressed these limitations. However, it is nontrivial to merely adapt DETR to this task, encountering two major bottlenecks. (1) How to integrate point label information into the model and (2) How to select optimal decoder proposals for training in the absence of complete action segment annotations. To address this issue, we introduce an end-to-end framework by integrating Query Reformation and Optimal Transport (QROT). Specifically, we encode point labels through a set of semantic consensus queries, enabling effective focus on action-relevant snippets. Furthermore, we integrate an optimal transport mechanism to generate high-quality pseudo labels. These pseudo-labels facilitate precise proposals selection based on Hungarian algorithm, significantly enhancing localization accuracy in point-supervised settings. Extensive experiments on the THUMOS14 and ActivityNet-v1.3 datasets demonstrate that our method outperforms existing MIL-based approaches, offering more stable and accurate temporal action localization in point-level supervision. | [
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298 | SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models | [
"Subhadeep Koley",
"Tapas Kumar Dutta",
"Aneeshan Sain",
"Pinaki Nath Chowdhury",
"Ayan Kumar Bhunia",
"Yi-Zhe Song"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Koley_SketchFusion_Learning_Universal_Sketch_Features_through_Fusing_Foundation_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Koley_SketchFusion_Learning_Universal_Sketch_Features_through_Fusing_Foundation_Models_CVPR_2025_paper.pdf | null | http://arxiv.org/abs/2503.14129 | @InProceedings{Koley_2025_CVPR,
author = {Koley, Subhadeep and Dutta, Tapas Kumar and Sain, Aneeshan and Chowdhury, Pinaki Nath and Bhunia, Ayan Kumar and Song, Yi-Zhe},
title = {SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2556-2567}
} | While foundation models have revolutionised computer vision, their effectiveness for sketch understanding remains limited by the unique challenges of abstract, sparse visual inputs. Through systematic analysis, we uncover two fundamental limitations: Stable Diffusion (SD) struggles to extract meaningful features from abstract sketches (unlike its success with photos), and exhibits a pronounced frequency-domain bias that suppresses essential low-frequency components needed for sketch understanding. Rather than costly retraining, we address these limitations by strategically combining SD with CLIP, whose strong semantic understanding naturally compensates for SD's spatial-frequency biases. By dynamically injecting CLIP features into SD's denoising process and adaptively aggregating features across semantic levels, our method achieves state-of-the-art performance in sketch retrieval (+3.35%), recognition (+1.06%), segmentation (+29.42%), and correspondence learning (+21.22%), demonstrating the first truly universal sketch feature representation in the era of foundation models. | [
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299 | Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior | [
"Haitao Wu",
"Qing Li",
"Changqing Zhang",
"Zhen He",
"Xiaomin Ying"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Bridging_the_Vision-Brain_Gap_with_an_Uncertainty-Aware_Blur_Prior_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Bridging_the_Vision-Brain_Gap_with_an_Uncertainty-Aware_Blur_Prior_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Bridging_the_Vision-Brain_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.04207 | @InProceedings{Wu_2025_CVPR,
author = {Wu, Haitao and Li, Qing and Zhang, Changqing and He, Zhen and Ying, Xiaomin},
title = {Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2246-2257}
} | Can our brain signals faithfully reflect the original visual stimuli, even including high-frequency details? Although human perceptual and cognitive capacities enable us to process and remember visual information, these abilities are constrained by several factors, such as limited attentional resources and finite capacity of visual memory. When visual stimuli are processed by human visual system into brain signals, some information is inevitably lost, leading to a discrepancy known as the System GAP. Additionally, perceptual and cognitive dynamics, along with technical noise in signal acquisition, degrade the fidelity of brain signals relative to the visual stimuli, known as the Random GAP. When encoded brain representations are directly aligned with the corresponding pretrained image features, the System GAP and Random GAP between paired data challenge the model, requiring it to bridge these gaps. However, due to limited paired data, these gaps are difficult for the model to learn, leading to overfitting and poor generalization to new data. To address these GAPs, we propose a simple yet effective approach called the Uncertainty-aware Blur Prior (UBP). It estimates the uncertainty within the paired data, reflecting the mismatch between brain signals and visual stimuli. Based on uncertainty, UBP dynamically blurs the high-frequency details of the original images, reducing the impact of mismatch and improving alignment. Our method achieves a top-1 accuracy of 50.9% and a top-5 accuracy of 79.7% on the zero-shot brain-to-image retrieval task, surpassing previous state-of-the-art methods. Code is available at https://github.com/HaitaoWuTJU/Uncertainty-aware-Blur-Prior. | [
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