Summary of Vidcomposition: Can Mllms Analyze Compositions in Compiled Videos?, by Yunlong Tang et al.
VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?
by Yunlong Tang, Junjia Guo, Hang Hua, Susan Liang, Mingqian Feng, Xinyang Li, Rui Mao, Chao Huang, Jing Bi, Zeliang Zhang, Pooyan Fazli, Chenliang Xu
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a new benchmark, VidComposition, to evaluate the video composition understanding capabilities of Multimodal Large Language Models (MLLMs). Existing benchmarks primarily focus on abstract video comprehension, whereas VidComposition assesses MLLMs’ ability to understand how visual elements combine and interact within compiled videos. The benchmark consists of 982 videos with 1706 multiple-choice questions, covering various compositional aspects such as camera movement, angle, shot size, narrative structure, character actions and emotions, etc. A comprehensive evaluation of 33 open-source and proprietary MLLMs reveals a significant performance gap between human and model capabilities, highlighting the limitations of current MLLMs in understanding complex video compositions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to test how well computers understand videos by looking at the way different parts work together. Current tests mostly focus on what’s happening in the video, but this new test looks at how all the different elements like camera angles and character actions fit together. The test has 982 videos with over 1700 questions that cover things like camera movement, lighting, and how characters are feeling. When they tested 33 computer models, they found that humans do much better than computers on this type of task, showing where computers need to improve. |
