Datasets:
Commit ·
ae61899
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Parent(s):
Duplicate from MME-Benchmarks/Video-MME-v2
Browse filesCo-authored-by: Haozhi Yuan <EliYuan00@users.noreply.huggingface.co>
- .gitattributes +61 -0
- README.md +144 -0
- assets/demo.mp4 +3 -0
- assets/demo_cover.png +3 -0
- assets/logo.png +3 -0
- assets/teaser.png +3 -0
- subtitle.zip +3 -0
- test.parquet +3 -0
- videos/001.zip +3 -0
- videos/002.zip +3 -0
- videos/003.zip +3 -0
- videos/004.zip +3 -0
- videos/005.zip +3 -0
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# Video files - compressed
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README.md
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| 1 |
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---
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license: mit
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task_categories:
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- video-text-to-text
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language:
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- en
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tags:
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- benchmark
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- video
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- multimodal
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- MCQ
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pretty_name: Video-MME-v2
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size_categories:
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- 1K<n<10K
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---
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<p align="center">
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<img src="assets/logo.png" width="100%" height="100%" alt="Video-MME-v2 logo">
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</p>
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<div align="center">
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[](https://video-mme-v2.netlify.app)
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[](https://arxiv.org/abs/2604.05015)
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[](https://github.com/MME-Benchmarks/Video-MME-v2)
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[](https://video-mme-v2.netlify.app/#leaderboard)
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</div>
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<!-- <p align="center">
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<a href="https://video-mme-v2.netlify.app/">🍎 Project Page</a> |
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<a href="https://arxiv.org/abs/2604.05015">📖 Paper</a> |
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<a href="https://huggingface.co/datasets/MME-Benchmarks/Video-MME-v2">🤗 Dataset</a> |
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<a href="https://video-mme-v2.netlify.app/#leaderboard">🏆 Leaderboard</a>
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</p> -->
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---
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# 🤗 About This Repo
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This repository contains annotation data for "[Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding](https://arxiv.org/abs/2604.05015)". It mainly consists of three parts: `videos/`, `test.parquet`, and `subtitle.zip`.
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- `videos/` contains **800 1080p MP4 files**, organized sequentially into 40 zip archives. For example, `001.mp4` to `020.mp4` are stored in `001.zip`.
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- `test.parquet` contains **3200 QA instances**, with each video paired with **4 questions**. Each instance includes the **question**, **options**, **answer**, and auxiliary metadata such as the **video id** and **task type**.
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- `subtitle.zip` contains **800 JSONL files**, each corresponding to a unique **video id**, with word-level entries and timestamps.
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---
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| 50 |
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# 🩷 About This Benchmark
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In 2024, our [**Video-MME**](https://video-mme.github.io/) benchmark became a standard evaluation set for frontier models like Gemini and GPT. However, as model capabilities rapidly evolve, scores on existing benchmarks are saturating, yet a clear gap remains between **leaderboard performance and actual user experience**. This indicates that current evaluation paradigms fail to capture true video understanding abilities. To address this, we spent a year redesigning the evaluation system from first principles and now introduce **Video-MME v2**—a progressive and robust benchmark designed to drive the next generation of video understanding models.
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<p align="center">
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<img src="assets/teaser.png" width="100%" height="100%" alt="Teaser">
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</p>
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| 58 |
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- **Dataset Size**
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| 60 |
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The dataset consists of 800 videos and 3,200 QA pairs, with each video associated with four MCQ-based questions.
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- **Multi-level Evaluation Hierarchy**
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- 🔍 **Level 1:** Retrieval & Aggregation
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- ⏱️ **Level 2:** Level 1 + Temporal Understanding
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- 🧠 **Level 3:** Level 2 + Complex Reasoning.
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- **Group-based Evaluation Strategy**
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- **Capability consistency groups** examine the breadth of a specific fundamental perception skill.
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- **Reasoning coherence groups** assess the depth of a model’s reasoning ability.
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- **Video Sources**
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All videos are collected from YouTube. Over 80% were published in 2025 or later, with nearly 40% published after October 2025.
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- **Video Categories**
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The dataset includes four top-level domains, further divided into 31 fine-grained subcategories.
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- **Metrics**
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A non-linear scoring mechanism is applied to all question groups, and a first error truncation mechanism is used for reasoning coherence groups.
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---
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# 🍺 About a Concrete Case
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> **💡 Why this example matters?**
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> This video QA group demonstrates our **Reasoning Coherence** evaluation strategy and **Multi-level Hierarchy**. To answer the final state correctly, a model must successfully track the object backwards through temporal swaps. If a model guesses the initial state correctly but fails the intermediate swaps, our **first error truncation mechanism** will accurately penalize it for flawed reasoning.
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<p align="left">
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<a href="https://huggingface.co/datasets/MME-Benchmarks/Video-MME-v2/resolve/main/assets/demo.mp4">
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<img src="assets/demo_cover.png" width="45%" alt="Demo video cover"/>
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</a>
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</p>
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<p align="left">
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<strong>👆 Click the cover image to view the demo video.</strong>
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</p>
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<p>
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<strong>Q1:</strong> Did the ball exist underneath any of the shells?<br>
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A. No.<br>
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B. Yes. ✅<br>
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C. Cannot be determined.
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</p>
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<p>
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<strong>Q2:</strong> Underneath which shell was the ball located at the end?<br>
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A. There is no ball under any shell.<br>
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B. The third shell.<br>
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C. The sixth shell.<br>
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D. The second shell.<br>
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E. The seventh shell.<br>
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F. The fifth shell.<br>
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G. The fourth shell. ✅<br>
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H. The first shell.
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</p>
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<p>
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<strong>Q3:</strong> The host performed a total of two shell swaps (defining a single swap as an instance where all shells return to an approximately straight line). Underneath which shell was the ball located after the first swap?<br>
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A. There is no ball under any shell.<br>
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B. The seventh shell.<br>
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C. The fourth shell. ✅<br>
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D. The fifth shell.<br>
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E. The sixth shell.<br>
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F. The second shell.<br>
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G. The third shell.<br>
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H. The first shell.
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</p>
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<p>
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<strong>Q4:</strong> The host performed a total of two shell swaps (defining a single swap as an instance where all shells return to an approximately straight line). Underneath which shell was the ball located initially?<br>
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A. The seventh shell.<br>
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B. The fourth shell.<br>
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C. The fifth shell.<br>
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D. The third shell. ✅<br>
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E. The second shell.<br>
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F. There is no ball under any shell.<br>
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G. The first shell.<br>
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H. The sixth shell.
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</p>
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assets/demo.mp4
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version https://git-lfs.github.com/spec/v1
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oid sha256:c6b8f24056743a564410897bb75216bc91c48355cd73f06d6368565f55fed7b7
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size 141093652
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assets/demo_cover.png
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Git LFS Details
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assets/logo.png
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assets/teaser.png
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Git LFS Details
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subtitle.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:adbd3cfd98bd03756398d1c8b63c7bcddf0e5c2494b6a0736ed890456021c287
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size 7854449
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test.parquet
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version https://git-lfs.github.com/spec/v1
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