Weakly-supervised temporal action localization is a very challenging problem
because frame-wise labels are not given in the training stage while the only
hint is video-level labels: whether each video contains action frames of
interest. Previous methods aggregate frame-level class scores to produce
video-level prediction and learn from video-level action labels. This
formulation does not fully model the problem in that background frames are
forced to be misclassified as action classes to predict video-level labels
accurately. In this paper, we design Background Suppression Network (BaS-Net)
which introduces an auxiliary class for background and has a two-branch
weight-sharing architecture with an asymmetrical training strategy. This
enables BaS-Net to suppress activations from background frames to improve
localization performance. Extensive experiments demonstrate the effectiveness
of BaS-Net and its superiority over the state-of-the-art methods on the most
popular benchmarks - THUMOS'14 and ActivityNet. Our code and the trained model
are available at this https URL

本文提出背景抑制网络 (BaS-Net) 来解决仅具备视频层级标签、而没有帧级标签的弱监督时序动作定位问题；该网络结构采用两个分支和非对称训练策略来抑制背景帧的激活，有效地提高了动作的定位表现。实验验证表明，BaS-Net 在 THUMOS'14 和 ActivityNet 等基准测试上的表现优于当前最先进的方法。