Abstract
This paper presents FluxMem, a training-free framework for efficient streaming video understanding. FluxMem adaptively compresses redundant visual memory through a hierarchical, two-stage design: (1) a Temporal Adjacency Selection (TAS) module removes redundant visual tokens across adjacent frames, and (2) a Spatial Domain Consolidation (SDC) module further merges spatially repetitive regions within each frame into compact representations. To adapt effectively to dynamic scenes, we introduce a self-adaptive token compression mechanism in both TAS and SDC, which automatically determines the compression rate based on intrinsic scene statistics rather than manual tuning. Extensive experiments demonstrate that FluxMem achieves new state-of-the-art results on existing online video benchmarks, reaching 76.4 on StreamingBench and 67.2 on OVO-Bench under real-time settings, while reducing latency by 69.9% and peak GPU memory by 34.5% on OVO-Bench. Furthermore, it maintains strong offline performance, achieving 73.1 on MLVU while using 65% fewer visual tokens.
Abstract (translated)
本文介绍了FluxMem，这是一种无需训练的高效流媒体视频理解框架。FluxMem通过一个分层、两阶段的设计自适应地压缩冗余视觉记忆：（1）时序邻近选择(TAS)模块删除相邻帧之间的冗余视觉标记；（2）空间领域合并(SDC)模块进一步将每个帧内的重复空间区域合并为紧凑的表示。为了有效适应动态场景，我们在TAS和SDC中引入了一种自适应令牌压缩机制，该机制可以根据内在场景统计自动确定压缩率，而不是手动调整。 广泛的实验表明，在现有的在线视频基准测试上，FluxMem达到了新的最佳性能：在实时设置下，在StreamingBench上的得分为76.4分，在OVO-Bench上的得分为67.2分，并且在OVO-Bench中延迟减少了69.9%，峰值GPU内存使用量减少了34.5%。此外，它还保持了强大的离线性能，在MLVU上的得分达到了73.1，同时使用的视觉令牌数量减少了65%。
URL
https://arxiv.org/abs/2603.02096
