Core Concepts
MA-LMM introduces a long-term memory bank to efficiently and effectively model long-term video sequences by processing frames in an online manner and storing historical video information, addressing the limitations of current large multimodal models.
Abstract
The paper introduces MA-LMM, a memory-augmented large multimodal model for efficient and effective long-term video understanding. The key contributions are:
Long-term Memory Bank Design:
MA-LMM processes video frames sequentially and stores the visual features and learned queries in two separate memory banks.
The memory banks allow the model to reference historical video content for long-term analysis without exceeding the context length constraints or GPU memory limits of large language models (LLMs).
The memory bank can be seamlessly integrated into current multimodal LLMs in an off-the-shelf manner.
Online Video Processing:
Unlike existing methods that process all video frames simultaneously, MA-LMM processes video frames in an online manner, significantly reducing the GPU memory footprint for long video sequences.
This online processing approach effectively addresses the constraints posed by the limited context length in LLMs.
Memory Bank Compression:
To maintain the length of the memory bank constant relative to the input video length, MA-LMM proposes a memory bank compression method that selects and averages the most similar adjacent frame features.
This compression technique preserves the temporal information while significantly reducing the redundancies in long videos.
The paper conducts extensive experiments on various video understanding tasks, including long-term video understanding, video question answering, and video captioning. MA-LMM achieves state-of-the-art performances across multiple datasets, demonstrating its superior capabilities in efficiently and effectively modeling long-term video sequences.
Stats
The paper does not provide any specific numerical data or statistics to support the key logics. The focus is on the model design and architecture.