Flash-VStream: Efficient Real-Time Understanding for Long Video Streams
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
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.
