Background Suppression Network for Weakly-Supervised Temporal Action Localization

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-theart methods on the most popular benchmarks – THUMOS’14 and ActivityNet. Our code and the trained model are available at https: //github. com/Pilhyeon/BaSNet-pytorch.

Authors

• Pilhyeon Lee
• Youngjung Uh Yonsei University
• Hyeran Byun

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