M-LLM Based Video Frame Selection for Efficient Video Understanding

• URL: http://arxiv.org/abs/2502.19680v1
• Date: Thu, 27 Feb 2025 01:44:13 GMT
• Title: M-LLM Based Video Frame Selection for Efficient Video Understanding
• Authors: Kai Hu, Feng Gao, Xiaohan Nie, Peng Zhou, Son Tran, Tal Neiman, Lingyun Wang, Mubarak Shah, Raffay Hamid, Bing Yin, Trishul Chilimbi,
• Abstract summary: We propose a light-weight M-LLM-based frame selection method that adaptively select frames that are more relevant to users' queries.<n>The selected frames are then digested by a frozen downstream video M-LLM for visual reasoning and question answering.
• Score: 60.93714759178143
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Multi-Modal Large Language Models (M-LLMs) show promising results in video reasoning. Popular Multi-Modal Large Language Model (M-LLM) frameworks usually apply naive uniform sampling to reduce the number of video frames that are fed into an M-LLM, particularly for long context videos. However, it could lose crucial context in certain periods of a video, so that the downstream M-LLM may not have sufficient visual information to answer a question. To attack this pain point, we propose a light-weight M-LLM -based frame selection method that adaptively select frames that are more relevant to users' queries. In order to train the proposed frame selector, we introduce two supervision signals (i) Spatial signal, where single frame importance score by prompting a M-LLM; (ii) Temporal signal, in which multiple frames selection by prompting Large Language Model (LLM) using the captions of all frame candidates. The selected frames are then digested by a frozen downstream video M-LLM for visual reasoning and question answering. Empirical results show that the proposed M-LLM video frame selector improves the performances various downstream video Large Language Model (video-LLM) across medium (ActivityNet, NExT-QA) and long (EgoSchema, LongVideoBench) context video question answering benchmarks.

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