  Weakly supervised temporal action localization aims to localize temporal
boundaries of actions and simultaneously identify their categories with only
video-level category labels. Many existing methods seek to generate pseudo
labels for bridging the discrepancy between classification and localization,
but usually only make use of limited contextual information for pseudo label
generation. To alleviate this problem, we propose a representative snippet
summarization and propagation framework. Our method seeks to mine the
representative snippets in each video for propagating information between video
snippets to generate better pseudo labels. For each video, its own
representative snippets and the representative snippets from a memory bank are
propagated to update the input features in an intra- and inter-video manner.
The pseudo labels are generated from the temporal class activation maps of the
updated features to rectify the predictions of the main branch. Our method
obtains superior performance in comparison to the existing methods on two
benchmarks, THUMOS14 and ActivityNet1.3, achieving gains as high as 1.2% in
terms of average mAP on THUMOS14.
