MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding
<|reference_start|>MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding: 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. Code available at https://boheumd.github.io/MA-LMM/.<|reference_end|>
@article{he2024ma-lmm:,
    title={MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video
  Understanding},
    author={Bo He, Hengduo Li, Young Kyun Jang, Menglin Jia, Xuefei Cao, Ashish
  Shah, Abhinav Shrivastava, Ser-Nam Lim},
    journal={arXiv preprint arXiv:2404.05726},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05726},
    primaryClass={cs.CV}
}