Summary of Mlvu: Benchmarking Multi-task Long Video Understanding, by Junjie Zhou et al.
MLVU: Benchmarking Multi-task Long Video Understanding
by Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Zhengyang Liang, Shitao Xiao, Minghao Qin, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, Zheng Liu
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Multi-task Long Video Understanding Benchmark (MLVU) addresses the limitations of existing benchmarks for evaluating Long Video Understanding (LVU) performance. MLVU features flexible video lengths, diverse genres (e.g., movies, surveillance footage), and varied evaluation tasks to comprehensively assess MLLMs’ abilities in long-video understanding. The empirical study with 23 latest MLLMs reveals significant room for improvement, highlighting the importance of factors like context length, image-understanding ability, and LLM backbone choice in future advancements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new benchmark called MLVU is proposed to improve the evaluation of Long Video Understanding (LVU) performance. The benchmark has longer videos, different types of videos, and more tasks to test how well AI models understand long videos. This helps researchers develop better models that can handle long videos. The study shows that current models are not very good at understanding long videos and need improvement. |
Keywords
» Artificial intelligence » Context length » Multi task
