Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding
With the rapid advancement of video understanding, existing benchmarks are becoming increasingly saturated, exposing a critical discrepancy between inflated leaderboard scores and real-world model capabilities. To address this widening gap, we introduce Video-MME-v2, a comprehensive benchmark designed to rigorously evaluate the robustness and faithfulness of video understanding. To systematically evaluate model capabilities, we design a \textbf{progressive tri-level hierarchy} that incrementally increases the complexity of video comprehension, ranging from multi-point visual information aggregation, to temporal dynamics modeling, and ultimately to complex multimodal reasoning. Besides, in...
| Authors | Chaoyou Fu, Haozhi Yuan, Yuhao Dong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
Models and datasets referencing this paper
- MME-Benchmarks/Video-MME-v2Dataset
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