  Point-supervised Temporal Action Localization (PSTAL) is an emerging research
direction for label-efficient learning. However, current methods mainly focus
on optimizing the network either at the snippet-level or the instance-level,
neglecting the inherent reliability of point annotations at both levels. In
this paper, we propose a Hierarchical Reliability Propagation (HR-Pro)
framework, which consists of two reliability-aware stages: Snippet-level
Discrimination Learning and Instance-level Completeness Learning, both stages
explore the efficient propagation of high-confidence cues in point annotations.
For snippet-level learning, we introduce an online-updated memory to store
reliable snippet prototypes for each class. We then employ a Reliability-aware
Attention Block to capture both intra-video and inter-video dependencies of
snippets, resulting in more discriminative and robust snippet representation.
For instance-level learning, we propose a point-based proposal generation
approach as a means of connecting snippets and instances, which produces
high-confidence proposals for further optimization at the instance level.
Through multi-level reliability-aware learning, we obtain more reliable
confidence scores and more accurate temporal boundaries of predicted proposals.
Our HR-Pro achieves state-of-the-art performance on multiple challenging
benchmarks, including an impressive average mAP of 60.3% on THUMOS14. Notably,
our HR-Pro largely surpasses all previous point-supervised methods, and even
outperforms several competitive fully supervised methods. Code will be
available at https://github.com/pipixin321/HR-Pro.
