Token-Efficient Long Video Understanding for Multimodal LLMs
Recent advances in video-based multimodal large language models (Video-LLMs)
have significantly improved video understanding by processing videos as
sequences of image frames. However, many existing methods treat frames
independently in the vision backbone, lacking explicit temporal modeling, which
limits their ability to capture dynamic patterns and efficiently handle long
videos. To address these limitations, we introduce STORM
(Spatiotemporal TOken Reduction for
Multimodal LLMs), a novel architecture incorporating a dedicated
temporal encoder between the image encoder and the LLM. Our temporal encoder
leverages the Mamba State Space Model to integrate temporal information into
image tokens, generating enriched representations that preserve inter-frame
dynamics across the entire video sequence. This enriched encoding not only
enhances video reasoning capabilities but also enables effective token
reduction strategies, including test-time sampling and training-based temporal
and spatial pooling, substantially reducing computational demands on the LLM
without sacrificing key temporal information. By integrating these techniques,
our approach simultaneously reduces training and inference latency while
improving performance, enabling efficient and robust video understanding over
extended temporal contexts. Extensive evaluations show that STORM achieves
state-of-the-art results across various long video understanding benchmarks
(more than 5\% improvement on MLVU and LongVideoBench) while reducing the
computation costs by up to 8times and the decoding latency by
2.4-2.9times for the fixed numbers of input frames. Project page is
available at https://research.nvidia.com/labs/lpr/storm
67ca7baf6d5c2eafede56d35
Jindong Jiang
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2025-03-07T09:34:59.750Z
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Jindong Jiang
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Xiuyu Li
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Xiuyu Li
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Zhijian Liu
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Zhijian Liu
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Muyang Li
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Muyang Li
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Guo Chen
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Guo Chen
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Zhiqi Li
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2025-03-07T09:36:57.283Z
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Zhiqi Li
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De-An Huang
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De-An Huang
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Guilin Liu
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GuilinLiu
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Zhiding Yu
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2025-03-07T09:36:09.646Z
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Zhiding Yu
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Kurt Keutzer
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2025-03-07T09:36:02.351Z
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Kurt Keutzer
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Sungjin Ahn
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Jan Kautz
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Hongxu Yin
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Hongxu Yin
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Yao Lu
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Song Han
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Wonmin Byeon
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Wonmin Byeon
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Token-Efficient Long Video Understanding for Multimodal LLMs
Recent advances in video-based multimodal large language models (Video-LLMs)
have significantly improved video understanding by processing videos as
sequences of image frames. However, many existing methods treat frames
independently in the vision backbone, lacking explicit temporal modeling, which
limits their ability to capture dynamic patterns and efficiently handle long
videos. To address these limitations, we introduce STORM
(Spatiotemporal TOken Reduction for
Multimodal LLMs), a novel architecture incorporating a dedicated
temporal encoder between the image encoder and the LLM. Our temporal encoder
leverages the Mamba State Space Model to integrate temporal information into
image tokens, generating enriched representations that preserve inter-frame
dynamics across the entire video sequence. This enriched encoding not only
enhances video reasoning capabilities but also enables effective token
reduction strategies, including test-time sampling and training-based temporal
and spatial pooling, substantially reducing computational demands on the LLM
without sacrificing key temporal information. By integrating these techniques,
our approach simultaneously reduces training and inference latency while
improving performance, enabling efficient and robust video understanding over
extended temporal contexts. Extensive evaluations show that STORM achieves
state-of-the-art results across various long video understanding benchmarks
(more than 5\% improvement on MLVU and LongVideoBench) while reducing the
computation costs by up to 8times and the decoding latency by
2.4-2.9times for the fixed numbers of input frames. Project page is
available at https://research.nvidia.com/labs/lpr/storm
STORM integrates a temporal encoder using the Mamba State Space Model to enhance video understanding and reduce computational costs in multimodal large language models.
Spatiotemporal Token Reduction temporal encoder Mamba State Space Model image tokens inter-frame dynamics token reduction test-time sampling training-based temporal and spatial pooling video reasoning video understanding MLVU LongVideoBench