  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.
