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Flash-VStream: Efficient Real-Time Understanding for Long Video Streams

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arxiv 2506.23825 v2 pith:C5VS42FP submitted 2025-06-30 cs.CV

Flash-VStream: Efficient Real-Time Understanding for Long Video Streams

Haoji Zhang , Yiqin Wang , Yansong Tang , Yong Liu , Jiashi Feng , Xiaojie Jin This is my paper
classification cs.CV
keywords longvideovideosflash-vstreammemoryexistinginformationlanguage
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Benefiting from the advances in large language models and cross-modal alignment, existing multimodal large language models have achieved prominent performance in image and short video understanding. However, the understanding of long videos is still challenging, as their long-context nature results in significant computational and memory overhead. Most existing work treats long videos in the same way as short videos, which is inefficient for real-world applications and hard to generalize to even longer videos. To address these issues, we propose Flash-VStream, an efficient video language model capable of processing extremely long videos and responding to user queries in real time. Particularly, we design a Flash Memory module, containing a low-capacity context memory to aggregate long-context temporal information and model the distribution of information density, and a high-capacity augmentation memory to retrieve detailed spatial information based on this distribution. Compared to existing models, Flash-VStream achieves significant reductions in inference latency. Extensive experiments on long video benchmarks and comprehensive video benchmarks, i.e., EgoSchema, MLVU, LVBench, MVBench and Video-MME, demonstrate the state-of-the-art performance and outstanding efficiency of our method. Code is available at https://github.com/IVGSZ/Flash-VStream.

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Cited by 8 Pith papers

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