Most of the current action localization methods follow an anchor-based
pipeline: depicting action instances by pre-defined anchors, learning to select
the anchors closest to the ground truth, and predicting the confidence of
anchors with refinements. Pre-defined anchors set prior about the location and
duration for action instances, which facilitates the localization for common
action instances but limits the flexibility for tackling action instances with
drastic varieties, especially for extremely short or extremely long ones. To
address this problem, this paper proposes a novel anchor-free action
localization module that assists action localization by temporal points.
Specifically, this module represents an action instance as a point with its
distances to the starting boundary and ending boundary, alleviating the
pre-defined anchor restrictions in terms of action localization and duration.
The proposed anchor-free module is capable of predicting the action instances
whose duration is either extremely short or extremely long. By combining the
proposed anchor-free module with a conventional anchor-based module, we propose
a novel action localization framework, called A2Net. The cooperation between
anchor-free and anchor-based modules achieves superior performance to the
state-of-the-art on THUMOS14 (45.5% vs. 42.8%). Furthermore, comprehensive
experiments demonstrate the complementarity between the anchor-free and the
anchor-based module, making A2Net simple but effective.

本文提出了一种新颖的基于点而非预设 anchor 的动作定位模块，结合传统基于 anchor 的模块提出了一种新的动作定位框架 A2Net，实现了在 THUMOS14 上的业界领先表现，证明了基于点和基于 anchor 的模块间互补性。