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.

翻译：点监督时序动作定位（PSTAL）是标签高效学习中的一个新兴研究方向。然而，当前方法主要专注于在片段级或实例级优化网络，忽略了这两个层级中点标注的内在可靠性。本文提出一种分层可靠性传播（HR-Pro）框架，包含两个可靠性感知阶段：片段级判别学习与实例级完整性学习。两个阶段均探索了点标注中高置信度线索的高效传播。在片段级学习中，我们引入一个在线更新的记忆库，用于存储每个类别的可靠片段原型；进而采用可靠性感知注意力模块捕获片段间的视频内及视频间依赖关系，从而获得更具判别性和鲁棒性的片段表示。在实例级学习中，我们提出一种基于点的提案生成方法作为连接片段与实例的桥梁，通过生成高置信度提案用于实例级进一步优化。通过多级可靠性感知学习，我们获得了更可靠的置信度分数和更准确的预测提案时间边界。HR-Pro在多个具有挑战性的基准数据集上达到最先进性能，包括在THUMOS14上取得了60.3%的平均mAP。值得注意的是，HR-Pro大幅超越所有以往点监督方法，甚至超过若干有竞争力的全监督方法。代码将发布于https://github.com/pipixin321/HR-Pro。