Weakly-supervised temporal action localization aims to localize action
instances in videos with only video-level action labels. Existing methods
mainly embrace a localization-by-classification pipeline that optimizes the
snippet-level prediction with a video classification loss. However, this
formulation suffers from the discrepancy between classification and detection,
resulting in inaccurate separation of foreground and background (F\&B)
snippets. To alleviate this problem, we propose to explore the underlying
structure among the snippets by resorting to unsupervised snippet clustering,
rather than heavily relying on the video classification loss. Specifically, we
propose a novel clustering-based F\&B separation algorithm. It comprises two
core components: a snippet clustering component that groups the snippets into
multiple latent clusters and a cluster classification component that further
classifies the cluster as foreground or background. As there are no
ground-truth labels to train these two components, we introduce a unified
self-labeling mechanism based on optimal transport to produce high-quality
pseudo-labels that match several plausible prior distributions. This ensures
that the cluster assignments of the snippets can be accurately associated with
their F\&B labels, thereby boosting the F\&B separation. We evaluate our method
on three benchmarks: THUMOS14, ActivityNet v1.2 and v1.3. Our method achieves
promising performance on all three benchmarks while being significantly more
lightweight than previous methods. Code is available at
this https URL

基于弱监督学习的时间动作定位，通过只用视频级别的动作标签来定位视频中的动作实例。通过无监督的片段聚类，提出了一种基于聚类的前景与背景分割算法，通过产生匹配多个可能的先验分布的高质量伪标签，确保片段的聚类分配与其前景和背景标签的准确关联，从而改善前景和背景分离。在 THUMOS14、ActivityNet v1.2 和 v1.3 的三个基准上评估了该方法，相比之前的方法，我们的方法在所有三个基准上都取得了良好的性能，同时更加轻量级。