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Collections including paper arxiv:2411.14402

Papers - Multimodal

TinyLLaVA: A Framework of Small-scale Large Multimodal Models

Paper • 2402.14289 • Published Feb 22, 2024 • 19
ImageBind: One Embedding Space To Bind Them All

Paper • 2305.05665 • Published May 9, 2023 • 5
DocLLM: A layout-aware generative language model for multimodal document understanding

Paper • 2401.00908 • Published Dec 31, 2023 • 182
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts

Paper • 2206.02770 • Published Jun 6, 2022 • 3

A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions

Paper • 2312.08578 • Published Dec 14, 2023 • 17
ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks

Paper • 2312.08583 • Published Dec 14, 2023 • 9
Vision-Language Models as a Source of Rewards

Paper • 2312.09187 • Published Dec 14, 2023 • 12
StemGen: A music generation model that listens

Paper • 2312.08723 • Published Dec 14, 2023 • 48

Paper - Multimodal

Paper related to Multimodal Model - Research for a : Modular, Multimodal, Multi-Stream, Mixture of Expert, Universal Transformer, Matryoshka embedding

Flowing from Words to Pixels: A Framework for Cross-Modality Evolution

Paper • 2412.15213 • Published Dec 19, 2024 • 26
No More Adam: Learning Rate Scaling at Initialization is All You Need

Paper • 2412.11768 • Published Dec 16, 2024 • 41
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Paper • 2412.13663 • Published Dec 18, 2024 • 125
Autoregressive Video Generation without Vector Quantization

Paper • 2412.14169 • Published Dec 18, 2024 • 14

Multimodal Autoregressive Pre-training of Large Vision Encoders

Paper • 2411.14402 • Published Nov 21, 2024 • 43

Multimodal Autoregressive Pre-training of Large Vision Encoders

Paper • 2411.14402 • Published Nov 21, 2024 • 43

Multimodal Autoregressive Pre-training of Large Vision Encoders

Paper • 2411.14402 • Published Nov 21, 2024 • 43

MutiModal_Paper

PUMA: Empowering Unified MLLM with Multi-granular Visual Generation

Paper • 2410.13861 • Published Oct 17, 2024 • 53
JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation

Paper • 2411.07975 • Published Nov 12, 2024 • 28
Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization

Paper • 2411.10442 • Published Nov 15, 2024 • 73
Multimodal Autoregressive Pre-training of Large Vision Encoders

Paper • 2411.14402 • Published Nov 21, 2024 • 43

RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval

Paper • 2409.10516 • Published Sep 16, 2024 • 41
Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse

Paper • 2409.11242 • Published Sep 17, 2024 • 6
Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models

Paper • 2409.11136 • Published Sep 17, 2024 • 22
On the Diagram of Thought

Paper • 2409.10038 • Published Sep 16, 2024 • 13

LongVILA: Scaling Long-Context Visual Language Models for Long Videos

Paper • 2408.10188 • Published Aug 19, 2024 • 51
xGen-MM (BLIP-3): A Family of Open Large Multimodal Models

Paper • 2408.08872 • Published Aug 16, 2024 • 98
Building and better understanding vision-language models: insights and future directions

Paper • 2408.12637 • Published Aug 22, 2024 • 124
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Paper • 2408.12528 • Published Aug 22, 2024 • 51

Multimodal Language Model

What does matter besides data receipt when training a Multimodal language model?

LLaVA-OneVision: Easy Visual Task Transfer

Paper • 2408.03326 • Published Aug 6, 2024 • 60
VILA^2: VILA Augmented VILA

Paper • 2407.17453 • Published Jul 24, 2024 • 40
PaliGemma: A versatile 3B VLM for transfer

Paper • 2407.07726 • Published Jul 10, 2024 • 68
openbmb/MiniCPM-V-2_6

Image-Text-to-Text • Updated 16 days ago • 91.4k • 921

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