Memory-Augmented Large Multimodal Model for Long-Term Video Understanding

MA-LMM, the Memory-Augmented Large Multimodal Model, is addressing the limitations of existing LLM-based multimodal models like Video-LLaMA by proposing an efficient method for long-term video understanding. Instead of processing more frames simultaneously, MA-LMM introduces a memory bank allowing the model to reference historical video content within LLM’s context length constraints and GPU memory limits. This model has shown state-of-the-art performance on multiple datasets for long-video understanding, video question answering, and video captioning.

• The model successfully integrates vision to LLMs for foundation models.
• It processes videos online and archives past information without overburdening resources.
• MA-LMM is extensible to existing multimodal LLMs in a straightforward manner.
• Extensive experiments validate the model’s state-of-the-art performance on various tasks.
• Source code is available, enabling further innovation and replication of results.

The introduction of MA-LMM paves the way for breakthroughs in video content analysis and other long-term data understanding tasks. It underlines the significance of context and memory in multimodal LLMs, promising avenues for further research in more complex datasets and real-world applications.