  Weakly-supervised temporal action localization (WTAL) in untrimmed videos has
emerged as a practical but challenging task since only video-level labels are
available. Existing approaches typically leverage off-the-shelf segment-level
features, which suffer from spatial incompleteness and temporal incoherence,
thus limiting their performance. In this paper, we tackle this problem from a
new perspective by enhancing segment-level representations with a simple yet
effective graph convolutional network, namely action complement graph network
(ACGNet). It facilitates the current video segment to perceive spatial-temporal
dependencies from others that potentially convey complementary clues,
implicitly mitigating the negative effects caused by the two issues above. By
this means, the segment-level features are more discriminative and robust to
spatial-temporal variations, contributing to higher localization accuracies.
More importantly, the proposed ACGNet works as a universal module that can be
flexibly plugged into different WTAL frameworks, while maintaining the
end-to-end training fashion. Extensive experiments are conducted on the
THUMOS'14 and ActivityNet1.2 benchmarks, where the state-of-the-art results
clearly demonstrate the superiority of the proposed approach.
