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
this https URL

本文提出了一种利用文本信息来改进弱监督时间动作定位方法的算法，包括建立基于行为分类标签的文本描述，并通过文本分析找到与之相关的时间段以及通过视频分析来完整文本描述的生成目标。该方法在 THUMOS14 和 ActivityNet1.3 上取得了最领先性能，并且可以无缝地应用于现有方法，进一步提高其性能。