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Streaming video recognition reasons about objects and their actions in everyframe of a video. A good streaming recognition model captures both long-termdynamics and short-term changes of video. Unfortunately, in most existingmethods, the computational complexity grows linearly or quadratically with thelength of the considered dynamics. This issue is particularly pronounced intransformer-based architectures. To address this issue, we reformulate thecross-attention in a video transformer through the lens of kernel and apply twokinds of temporal smoothing kernel: A box kernel or a Laplace kernel. Theresulting streaming attention reuses much of the computation from frame toframe, and only requires a constant time update each frame. Based on this idea,we build TeSTra, a Temporal Smoothing Transformer, that takes in arbitrarilylong inputs with constant caching and computing overhead. Specifically, it runs$6\times$ faster than equivalent sliding-window based transformers with 2,048frames in a streaming setting. Furthermore, thanks to the increased temporalspan, TeSTra achieves state-of-the-art results on THUMOS'14 andEPIC-Kitchen-100, two standard online action detection and action anticipationdatasets. A real-time version of TeSTra outperforms all but one priorapproaches on the THUMOS'14 dataset.

Real-time Online Video Detection with Temporal Smoothing Transformers