Realigning Confidence with Temporal Saliency Information for Point-Level Weakly-Supervised Temporal Action Localization

Point-level weakly-supervised temporal action localization (P- TAL) aims to localize action instances in untrimmed videos through the use of single-point annotations in each instance. Existing methods predict the class activation se-quences without any boundary information, and the unreli-able sequences result in a significant misalignment between the quality of proposals and their corresponding confidence. In this paper, we surprisingly observe the most salientframe tend to appear in the central region of the each instance and is easily annotated by humans. Guided by the temporal saliency information, we present a novel proposal-level plug-in framework to relearn the aligned confidence of proposals generated by the base locators. The proposed approach consists of Center Score Learning (CSL) and Alignment-based Boundary Adaptation (ABA). In CSL, we design a novel center label generated by the point annotations for predicting aligned center scores. During inference, we first fuse the center scores with the predicted action probabilities to obtain the aligned confidence. ABA utilizes the both aligned confidence and IoU information to enhance localization completeness. Extensive experiments demon-strate the generalization and effectiveness of the proposed framework, showcasing state-of-the-art or competitive per-formances across three benchmarks. Our code is available at https://github.com/zyxial0091CVPR2024-TspNet.