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.

翻译：由于缺乏时序标注，当前的弱监督时序动作定位（WTAL）方法普遍陷入过完备或不完整定位的困境。本文旨在从两个维度利用文本信息提升WTAL性能：（a）通过判别性目标扩大类间差异，从而减少过完备问题；（b）通过生成性目标增强类内完整性，从而找到更完整的时序边界。针对判别性目标，我们提出文本-片段挖掘（TSM）机制，该机制基于动作类别标签构建文本描述，并将文本作为查询项来挖掘所有与类别相关的片段。在没有动作时序标注的情况下，TSM将文本查询与整个数据集中的视频进行对比，以挖掘最佳匹配片段，同时忽略无关片段。由于不同类别视频中存在共享子动作，仅使用TSM会过于严格地忽略语义相关片段，导致定位不完整。我们进一步引入名为视频-文本语言补全（VLC）的生成性目标，该目标聚焦视频中所有语义相关片段以补全文本句子。我们在THUMOS14和ActivityNet1.3数据集上取得了最先进性能。令人惊讶的是，我们发现所提出的方法可无缝应用于现有方法，并显著提升其性能。代码已开源至https://github.com/lgzlIlIlI/Boosting-WTAL。