Real-time Online Video Detection with Temporal Smoothing Transformers

• URL: http://arxiv.org/abs/2209.09236v1
• Date: Mon, 19 Sep 2022 17:59:02 GMT
• Title: Real-time Online Video Detection with Temporal Smoothing Transformers
• Authors: Yue Zhao and Philipp Kr\"ahenb\"uhl
• Abstract summary: A good streaming recognition model captures both long-term dynamics and short-term changes of video. To address this issue, we reformulate the cross-attention in a video transformer through the lens of kernel. We build TeSTra, a Temporal Smoothing Transformer, that takes in arbitrarily long inputs with constant caching and computing overhead.
• Score: 4.545986838009774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Streaming video recognition reasons about objects and their actions in every frame of a video. A good streaming recognition model captures both long-term dynamics and short-term changes of video. Unfortunately, in most existing methods, the computational complexity grows linearly or quadratically with the length of the considered dynamics. This issue is particularly pronounced in transformer-based architectures. To address this issue, we reformulate the cross-attention in a video transformer through the lens of kernel and apply two kinds of temporal smoothing kernel: A box kernel or a Laplace kernel. The resulting streaming attention reuses much of the computation from frame to frame, and only requires a constant time update each frame. Based on this idea, we build TeSTra, a Temporal Smoothing Transformer, that takes in arbitrarily long inputs with constant caching and computing overhead. Specifically, it runs $6\times$ faster than equivalent sliding-window based transformers with 2,048 frames in a streaming setting. Furthermore, thanks to the increased temporal span, TeSTra achieves state-of-the-art results on THUMOS'14 and EPIC-Kitchen-100, two standard online action detection and action anticipation datasets. A real-time version of TeSTra outperforms all but one prior approaches on the THUMOS'14 dataset.

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