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MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding

Bo He · Hengduo Li · Young Kyun Jang · Menglin Jia · Xuefei Cao · Ashish Shah · Abhinav Shrivastava · Ser-Nam Lim

Arch 4A-E Poster #372
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Thu 20 Jun 10:30 a.m. PDT — noon PDT


With the success of large language models (LLMs), integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However, existing LLM-based large multimodal models (e.g., Video-LLaMA, VideoChat) can only take in a limited number of frames for short video understanding. In this study, we mainly focus on designing an efficient and effective model for long-term video understanding. Instead of trying to process more frames simultaneously like most existing work, we propose to process videos in an online manner and store past video information in a memory bank. This allows our model to reference historical video content for long-term analysis without exceeding LLMs' context length constraints or GPU memory limits. Our memory bank can be seamlessly integrated into current multimodal LLMs in an off-the-shelf manner. We conduct extensive experiments on various video understanding tasks, such as long-video understanding, video question answering, and video captioning, and our model can achieve state-of-the-art performances across multiple datasets.

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