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MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding
LLMs, pretrained on extensive textual data, exhibit impressive capabilities in generative and discriminative tasks. Recent interest focuses on employing LLMs for multimodal tasks, integrating them with visual encoders for tasks like captioning, question answering, classification, and segmentation.
Challenges in Multimodal Models
Prior multimodal models face limitations in handling video inputs due to the context length restriction of LLMs and GPU memory constraints. This restricts their practicality for longer video durations such as movies or TV shows.
Practical Solutions
Researchers propose a Memory-Augmented Large Multimodal Model (MA-LMM) for efficient long-term video modeling. It follows the structure of existing multimodal models, featuring a visual encoder, a querying transformer, and a large language model. MA-LMM adopts an online processing approach, sequentially processing video frames and storing features in a long-term memory bank. This significantly reduces GPU memory usage for long video sequences and effectively addresses context length limitations in LLMs.
Advantages and Performance
MA-LMM demonstrates superior performance across various tasks compared to previous state-of-the-art methods. It outperforms existing models in long-term video understanding, video question answering, video captioning, and online action prediction tasks. MA-LMM’s innovative design enables efficient handling of long video sequences and achieves remarkable results even in challenging scenarios.
Practical Implementation
As demonstrated in experiments, the long-term memory bank is easily integrated into existing models and shows superior advantages across various tasks.
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