MLVU: Benchmarking Multi-task Long Video Understanding
Paper · 2024 · Junjie Zhou, Yan Shu, Bo Zhao et al.
Summary
Benchmarks for evaluating how multimodal LLMs (MLLMs) handle long videos have three main gaps. Most use clips lasting only seconds, and many cover a single genre or task. Their questions can also often be answered from one frame or from general knowledge of famous films. MLVU addresses these gaps with 1,730 videos ranging from 3 minutes to 2 hours (about 15 minutes on average). The videos span movies, documentaries, surveillance, egocentric recordings, sports, cartoons and games. Each video is also cut into nested segments, so the same task can be posed at different durations. Nine tasks are grouped into holistic, single-detail, multi-detail and open-ended types. Questions about local content describe the target moment rather than giving a timestamp. In tests of 23 MLLMs, even GPT-4o, the strongest model, averaged only 54.5% on multiple-choice tasks. Accuracy fell as videos grew longer. Counting, ordering and summarization were especially hard. The benchmark gives a more reliable way to diagnose long-video weaknesses and points to factors worth improving.
Key points
- MLVU contains 3,102 questions over 9 task categories, split into a dev set (2,593) and a test set (509).
- Task families:
- holistic: topic reasoning, anomaly recognition
- single-detail: Needle QA, Ego Reasoning, Plot QA
- multi-detail: action order, action count
- open-ended: video summarization, sub-scene captioning
- Recent open-source long-video models (e.g., Video-XL, LongVA) are narrowing the gap with proprietary systems. They are also less sensitive to where the relevant clip appears in the video.
- On action counting, every model's accuracy drops sharply as the number of details to track grows.
- Several factors are linked to better long-video scores:
- longer input context
- stronger image understanding, which tracks MMMU results
- larger or more advanced LLM backbones (Vicuna-13B over 7B, Mistral-7B over Llama-2-7B)
Institutions
Fields, topics and concepts
📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksComputer Vision and Pattern Recognition · 3,486 works
Cite
Cite as Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Zhengyang Liang, Shitao Xiao, Minghao Qin, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang and Zheng Liu. 2024. MLVU: Benchmarking Multi-task Long Video Understanding. arXiv:2406.04264.
@misc{zhou2024mlvu,
title = {{MLVU: Benchmarking Multi-task Long Video Understanding}},
author = {Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Zhengyang Liang, Shitao Xiao, Minghao Qin, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang and Zheng Liu},
year = {2024},
doi = {10.48550/arxiv.2406.04264},
eprint = {2406.04264},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2406.04264}
}References
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