  Weakly-supervised temporal action localization aims to localize actions in
untrimmed videos with only video-level action category labels. Most of previous
methods ignore the incompleteness issue of Class Activation Sequences (CAS),
suffering from trivial localization results. To solve this issue, we introduce
an adaptive mutual supervision framework (AMS) with two branches, where the
base branch adopts CAS to localize the most discriminative action regions,
while the supplementary branch localizes the less discriminative action regions
through a novel adaptive sampler. The adaptive sampler dynamically updates the
input of the supplementary branch with a sampling weight sequence negatively
correlated with the CAS from the base branch, thereby prompting the
supplementary branch to localize the action regions underestimated by the base
branch. To promote mutual enhancement between these two branches, we construct
mutual location supervision. Each branch leverages location pseudo-labels
generated from the other branch as localization supervision. By alternately
optimizing the two branches in multiple iterations, we progressively complete
action regions. Extensive experiments on THUMOS14 and ActivityNet1.2
demonstrate that the proposed AMS method significantly outperforms the
state-of-the-art methods.
