This technical report analyzes an egocentric video action detection method we
used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop. The
goal of our task is to locate the start time and the end time of the action in
the long untrimmed video, and predict action category. We adopt sliding window
strategy to generate proposals, which can better adapt to short-duration
actions. In addition, we show that classification and proposals are conflict in
the same network. The separation of the two tasks boost the detection
performance with high efficiency. By simply employing these strategy, we
achieved 16.10\% performance on the test set of EPIC-KITCHENS-100 Action
Detection challenge using a single model, surpassing the baseline method by
11.7\% in terms of average mAP.

本研究介绍了一种针对 2021 EPIC-KITCHENS-100 竞赛中的自我中心视频动作检测方法，其主要采用滑动窗口策略生成建议，以较高的效率提高任务的检测性能。通过简单地采用这些策略，我们使用单个模型在测试集上达到了 16.10％的性能，相对于基线方法平均 mAP 提高了 11.7％。