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--- |
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license: apache-2.0 |
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task_categories: |
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- visual-question-answering |
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- video-classification |
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language: |
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- en |
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viewer: false |
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configs: |
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- config_name: splits |
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data_files: |
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- split: eval |
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path: |
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- "video_tasks" |
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- "image_tasks" |
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--- |
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# MMEB-V2 (Massive Multimodal Embedding Benchmark) |
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Building upon on our original [**MMEB**](https://arxiv.org/abs/2410.05160), **MMEB-V2** expands the evaluation scope to include five new tasks: four video-based tasks — Video Retrieval, Moment Retrieval, Video Classification, and Video Question Answering — and one task focused on visual documents, Visual Document Retrieval. This comprehensive suite enables robust evaluation of multimodal embedding models across static, temporal, and structured visual data settings. |
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**This Hugging Face repository contains only the raw image and video files used in MMEB-V2, which need to be downloaded in advance.** |
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The test data for each task in MMEB-V2 is available [here](https://huggingface.co/VLM2Vec) and will be automatically downloaded and used by our code. More details on how to set it up are provided in the following sections. |
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|[**Github**](https://github.com/TIGER-AI-Lab/VLM2Vec) | [**🏆Leaderboard**](https://huggingface.co/spaces/TIGER-Lab/MMEB) | [**📖MMEB-V2/VLM2Vec-V2 Paper**](https://arxiv.org/abs/2507.04590) | | [**📖MMEB-V1/VLM2Vec-V1 Paper**](https://arxiv.org/abs/2410.05160) | |
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## 🚀 What's New |
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- **\[2025.07\]** Release [tech report](https://arxiv.org/abs/2507.04590). |
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- **\[2025.05\]** Initial release of MMEB-V2/VLM2Vec-V2. |
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## Dataset Overview |
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We present an overview of the MMEB-V2 dataset below: |
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<img width="900" alt="abs" src="overview.png"> |
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## Dataset Structure |
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The directory structure of this Hugging Face repository is shown below. |
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For video tasks, we provide both sampled frames and raw videos (the latter will be released later). For image tasks, we provide the raw images. |
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Files from each meta-task are zipped together, resulting in six files. For example, ``video_cls.tar.gz`` contains the sampled frames for the video classification task. |
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``` |
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→ video-tasks/ |
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├── frames/ |
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│ ├── video_cls.tar.gz |
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│ ├── video_qa.tar.gz |
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│ ├── video_ret.tar.gz |
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│ └── video_mret.tar.gz |
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├── raw videos/ (To be released) |
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→ image-tasks/ |
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├── mmeb_v1.tar.gz |
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└── visdoc.tar.gz |
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``` |
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After downloading and unzipping these files locally, you can organize them as shown below. (You may choose to use ``Git LFS`` or ``wget`` for downloading.) |
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Then, simply specify the correct file path in the configuration file used by your code. |
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``` |
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→ MMEB |
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├── video-tasks/ |
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│ └── frames/ |
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│ ├── video_cls/ |
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│ │ ├── UCF101/ |
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│ │ │ └── video_1/ # video ID |
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│ │ │ ├── frame1.png # frame from video_1 |
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│ │ │ ├── frame2.png |
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│ │ │ └── ... |
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│ │ ├── HMDB51/ |
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│ │ ├── Breakfast/ |
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│ │ └── ... # other datasets from video classification category |
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│ ├── video_qa/ |
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│ │ └── ... # video QA datasets |
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│ ├── video_ret/ |
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│ │ └── ... # video retrieval datasets |
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│ └── video_mret/ |
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│ └── ... # moment retrieval datasets |
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├── image-tasks/ |
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│ ├── mmeb_v1/ |
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│ │ ├── OK-VQA/ |
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│ │ │ ├── image1.png |
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│ │ │ ├── image2.png |
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│ │ │ └── ... |
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│ │ ├── ImageNet-1K/ |
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│ │ └── ... # other datasets from MMEB-V1 category |
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│ └── visdoc/ |
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│ └── ... # visual document retrieval datasets |
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``` |
