  Weakly-supervised Temporal Action Localization (WS-TAL) methods learn to
localize temporal starts and ends of action instances in a video under only
video-level supervision. Existing WS-TAL methods rely on deep features learned
for action recognition. However, due to the mismatch between classification and
localization, these features cannot distinguish the frequently co-occurring
contextual background, i.e., the context, and the actual action instances. We
term this challenge action-context confusion, and it will adversely affect the
action localization accuracy. To address this challenge, we introduce a
framework that learns two feature subspaces respectively for actions and their
context. By explicitly accounting for action visual elements, the action
instances can be localized more precisely without the distraction from the
context. To facilitate the learning of these two feature subspaces with only
video-level categorical labels, we leverage the predictions from both spatial
and temporal streams for snippets grouping. In addition, an unsupervised
learning task is introduced to make the proposed module focus on mining
temporal information. The proposed approach outperforms state-of-the-art WS-TAL
methods on three benchmarks, i.e., THUMOS14, ActivityNet v1.2 and v1.3
datasets.
