Multimodal models, which combine text and visual data, have shown impressive abilities in tasks like captioning, question answering, and classification. However, they faced challenges when dealing with longer video inputs such as movies or TV shows due to memory constraints.
Practical Solution:
Researchers have developed the Memory-Augmented Large Multimodal Model (MA-LMM) to efficiently handle long-term video modeling. This approach reduces GPU memory usage and effectively addresses context length limitations, allowing for the processing of longer video sequences.
Advantages and Performance:
MA-LMM outperforms existing models in tasks like long-term video understanding, video question answering, captioning, and online action prediction. Its innovative design enables efficient handling of long video sequences and delivers remarkable results even in challenging scenarios.
Practical Implementation:
Experiments have shown that the long-term memory bank of MA-LMM can be easily integrated into existing models, providing superior advantages across various tasks.
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