  Weakly-supervised temporal action localization aims to learn detecting
temporal intervals of action classes with only video-level labels. To this end,
it is crucial to separate frames of action classes from the background frames
(i.e., frames not belonging to any action classes). In this paper, we present a
new perspective on background frames where they are modeled as
out-of-distribution samples regarding their inconsistency. Then, background
frames can be detected by estimating the probability of each frame being
out-of-distribution, known as uncertainty, but it is infeasible to directly
learn uncertainty without frame-level labels. To realize the uncertainty
learning in the weakly-supervised setting, we leverage the multiple instance
learning formulation. Moreover, we further introduce a background entropy loss
to better discriminate background frames by encouraging their in-distribution
(action) probabilities to be uniformly distributed over all action classes.
Experimental results show that our uncertainty modeling is effective at
alleviating the interference of background frames and brings a large
performance gain without bells and whistles. We demonstrate that our model
significantly outperforms state-of-the-art methods on the benchmarks, THUMOS'14
and ActivityNet (1.2 & 1.3). Our code is available at
https://github.com/Pilhyeon/WTAL-Uncertainty-Modeling.
