Locating actions in long untrimmed videos has been a challenging problem in
video content analysis. The performances of existing action localization
approaches remain unsatisfactory in precisely determining the beginning and the
end of an action. Imitating the human perception procedure with observations
and refinements, we propose a novel three-phase action localization framework.
Our framework is embedded with an Actionness Network to generate initial
proposals through frame-wise similarity grouping, and then a Refinement Network
to conduct boundary adjustment on these proposals. Finally, the refined
proposals are sent to a Localization Network for further fine-grained location
regression. The whole process can be deemed as multi-stage refinement using a
novel non-local pyramid feature under various temporal granularities. We
evaluate our framework on THUMOS14 benchmark and obtain a significant
improvement over the state-of-the-arts approaches. Specifically, the
performance gain is remarkable under precise localization with high IoU
thresholds. Our proposed framework achieves mAP@IoU=0.5 of 34.2%.

提出了一个基于三阶段框架的行动定位方法，包括一个 Actionness 神经网络来生成初始提议，一个 Refinement 网络来进行边界调整，以及一个 Localization 网络进行精细定位回归。在 THUMOS14 基准测试中表现优异，尤其是在高 IoU 阈值下的精确定位方面表现出色，mAP@IoU=0.5 达到 34.2%。