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.