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FluxMem: Adaptive Hierarchical Memory for Streaming Video Understanding
🤖AI Summary
FluxMem is a new training-free framework for streaming video understanding that uses hierarchical memory compression to reduce computational costs. The system achieves state-of-the-art performance on video benchmarks while reducing latency by 69.9% and GPU memory usage by 34.5%.
Key Takeaways
- →FluxMem introduces a two-stage hierarchical design that removes redundant visual tokens across frames and merges repetitive spatial regions.
- →The framework achieves new state-of-the-art results on StreamingBench (76.4) and OVO-Bench (67.2) under real-time settings.
- →System reduces processing latency by 69.9% and peak GPU memory usage by 34.5% compared to existing methods.
- →The self-adaptive token compression mechanism automatically determines compression rates based on scene statistics without manual tuning.
- →FluxMem maintains strong offline performance achieving 73.1 on MLVU while using 65% fewer visual tokens.
#fluxmem#video-understanding#streaming#memory-optimization#computer-vision#ai-efficiency#real-time-processing#arxiv
Read Original →via arXiv – CS AI
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