Abstract
Online Action Detection (OAD) systems face two primary challenges: high computational cost and insufficient modeling of discriminative temporal dynamics against background motion. Adding optical flow could provides strong motion cues but it incurs significant computational overhead. We propose CAKE, a OAD Flow-based distillation framework to transfer motion knowledge into RGB models. We propose Dynamic Motion Adapter (DMA) to suppress static background noise and emphasize pixel changes, effectively approximating optical flow without explicit computation. The framework also integrates a Floating Contrastive Learning strategy to distinguish informative motion dynamics from temporal background. Various experiments conducted on the TVSeries, THUMOS'14, Kinetics-400 datasets show effectiveness of our model. CAKE achieves a standout mAP compared with SOTA while using the same backbone. Our model operates at over 72 FPS on a single CPU, making it highly suitable for resource-constrained systems.
Abstract (translated)
在线动作检测（OAD）系统面临两大主要挑战：计算成本高昂，以及对背景运动中的判别性时序动态建模不足。添加光流虽能提供强运动线索，但会带来巨大的计算开销。我们提出CAKE，一个基于光流蒸馏的OAD框架，用于将运动知识迁移至RGB模型。我们设计了动态运动适配器（DMA）以抑制静态背景噪声并突出像素变化，从而在无需显式计算的情况下有效近似光流。该框架还集成了浮动对比学习策略，以区分具有信息量的运动动态与时序背景。在TVSeries、THUMOS'14和Kinetics-400数据集上的多项实验表明了我们模型的有效性。在使用相同主干网络的情况下，CAKE实现了相较于SOTA方法的显著平均精度均值提升。我们的模型在单CPU上运行速度超过72 FPS，非常适合资源受限的系统。
URL
https://arxiv.org/abs/2603.23988
