  Due to the lack of temporal annotation, current Weakly-supervised Temporal
Action Localization (WTAL) methods are generally stuck into over-complete or
incomplete localization. In this paper, we aim to leverage the text information
to boost WTAL from two aspects, i.e., (a) the discriminative objective to
enlarge the inter-class difference, thus reducing the over-complete; (b) the
generative objective to enhance the intra-class integrity, thus finding more
complete temporal boundaries. For the discriminative objective, we propose a
Text-Segment Mining (TSM) mechanism, which constructs a text description based
on the action class label, and regards the text as the query to mine all
class-related segments. Without the temporal annotation of actions, TSM
compares the text query with the entire videos across the dataset to mine the
best matching segments while ignoring irrelevant ones. Due to the shared
sub-actions in different categories of videos, merely applying TSM is too
strict to neglect the semantic-related segments, which results in incomplete
localization. We further introduce a generative objective named Video-text
Language Completion (VLC), which focuses on all semantic-related segments from
videos to complete the text sentence. We achieve the state-of-the-art
performance on THUMOS14 and ActivityNet1.3. Surprisingly, we also find our
proposed method can be seamlessly applied to existing methods, and improve
their performances with a clear margin. The code is available at
https://github.com/lgzlIlIlI/Boosting-WTAL.
