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MME-Benchmarks/Video-MME-v2

πŸ”₯ News 2026.06.11 Videos re-encoded to H265, maintaining consistent evaluation scores. Fixed 2 incorrect MP4s & 3 mismatched URLs. Original data preserved in the original branch. 2026.05.22 Task types are now available for Q1-Q3 in coherence (logic) groups. πŸ€— About This Repo This repository contains annotation data for "Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding". It mainly consists of three… See the full description on the dataset page: https://huggingface.co/datasets/MME-Benchmarks/Video-MME-v2.

sourceHugging Facemitupdated 1mo agoView on Hugging Face
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Dataset Card

<p align="center"> <img src="assets/logo.png" width="100%" height="100%" alt="Video-MME-v2 logo"> </p>

<div align="center">

![Project](https://video-mme-v2.netlify.app) ![Paper](https://arxiv.org/abs/2604.05015) ![GitHub](https://github.com/MME-Benchmarks/Video-MME-v2) ![Leaderboard](https://video-mme-v2.netlify.app/#leaderboard)

</div>

<!-- <p align="center"> <a href="https://video-mme-v2.netlify.app/">🍎 Project Page</a> | <a href="https://arxiv.org/abs/2604.05015">πŸ“– Paper</a> | <a href="https://huggingface.co/datasets/MME-Benchmarks/Video-MME-v2">πŸ€— Dataset</a> | <a href="https://video-mme-v2.netlify.app/#leaderboard">πŸ† Leaderboard</a> </p> -->


πŸ”₯ News

  • β€”`2026.06.11` Videos re-encoded to H265, maintaining consistent evaluation scores. Fixed 2 incorrect MP4s & 3 mismatched URLs. Original data preserved in the original branch.
  • β€”`2026.05.22` Task types are now available for Q1-Q3 in coherence (logic) groups.

πŸ€— About This Repo

This repository contains annotation data for "Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding". It mainly consists of three parts: videos/, test.parquet, and subtitle.zip.

  • β€”videos/ contains 800 1080p MP4 files, organized sequentially into 40 zip archives. For example, 001.mp4 to 020.mp4 are stored in 001.zip.
  • β€”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.
  • β€”subtitle.zip contains 800 JSONL files, each corresponding to a unique video id, with word-level entries and timestamps.

🩷 About This Benchmark

In 2024, our **Video-MME** 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 v2β€”a progressive and robust benchmark designed to drive the next generation of video understanding models.

<p align="center"> <img src="assets/teaser.png" width="100%" height="100%" alt="Teaser"> </p>

  • β€”Dataset Size

The dataset consists of 800 videos and 3,200 QA pairs, with each video associated with four MCQ-based questions.

  • β€”Multi-level Evaluation Hierarchy
  • β€”πŸ” Level 1: Retrieval & Aggregation
  • —⏱️ Level 2: Level 1 + Temporal Understanding
  • β€”πŸ§  Level 3: Level 2 + Complex Reasoning.
  • β€”Group-based Evaluation Strategy
  • β€”Capability consistency groups examine the breadth of a specific fundamental perception skill.
  • β€”Reasoning coherence groups assess the depth of a model’s reasoning ability.
  • β€”Video Sources

All videos are collected from YouTube. Over 80% were published in 2025 or later, with nearly 40% published after October 2025.

  • β€”Video Categories

The dataset includes four top-level domains, further divided into 31 fine-grained subcategories.

  • β€”Metrics

A non-linear scoring mechanism is applied to all question groups, and a first error truncation mechanism is used for reasoning coherence groups.


🍺 About a Concrete Case

πŸ’‘ Why this example matters? 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.

<p align="left"> <a href="https://huggingface.co/datasets/MME-Benchmarks/Video-MME-v2/resolve/main/assets/demo.mp4"> <img src="assets/demo_cover.png" width="45%" alt="Demo video cover"/> </a> </p>

<p align="left"> <strong>πŸ‘† Click the cover image to view the demo video.</strong> </p>

<p> <strong>Q1:</strong> Did the ball exist underneath any of the shells?<br> A. No.<br> B. Yes. βœ…<br> C. Cannot be determined. </p>

<p> <strong>Q2:</strong> Underneath which shell was the ball located at the end?<br> A. There is no ball under any shell.<br> B. The third shell.<br> C. The sixth shell.<br> D. The second shell.<br> E. The seventh shell.<br> F. The fifth shell.<br> G. The fourth shell. βœ…<br> H. The first shell. </p>

<p> <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> A. There is no ball under any shell.<br> B. The seventh shell.<br> C. The fourth shell. βœ…<br> D. The fifth shell.<br> E. The sixth shell.<br> F. The second shell.<br> G. The third shell.<br> H. The first shell. </p>

<p> <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> A. The seventh shell.<br> B. The fourth shell.<br> C. The fifth shell.<br> D. The third shell. βœ…<br> E. The second shell.<br> F. There is no ball under any shell.<br> G. The first shell.<br> H. The sixth shell. </p>