Exploring the Temporal Consistency for Point-Level Weakly-Supervised Temporal Action Localization

2026-02-05

AI Summary

This work addresses the challenge in pointly supervised temporal action localization, where existing methods often fail to accurately localize complete action instances due to the lack of explicit modeling of temporal relationships among action frames. To this end, we propose a novel framework that, for the first time under the point supervision setting, incorporates temporal consistency modeling through a multi-task self-supervised learning paradigm. Specifically, the framework enhances the model’s perception of action structure by jointly optimizing three self-supervised tasks: action completion, sequential understanding, and regularity comprehension. Extensive experiments on four benchmark datasets demonstrate that our method significantly outperforms current state-of-the-art approaches, validating the effectiveness and generalizability of the proposed temporal consistency modeling mechanism.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Video Understanding & Activity Analysis
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Point-supervised Temporal Action Localization (PTAL) adopts a lightly frame-annotated paradigm (\textit{i.e.}, labeling only a single frame per action instance) to train a model to effectively locate action instances within untrimmed videos. Most existing approaches design the task head of models with only a point-supervised snippet-level classification, without explicit modeling of understanding temporal relationships among frames of an action. However, understanding the temporal relationships of frames is crucial because it can help a model understand how an action is defined and therefore benefits localizing the full frames of an action. To this end, in this paper, we design a multi-task learning framework that fully utilizes point supervision to boost the model's temporal understanding capability for action localization. Specifically, we design three self-supervised temporal understanding tasks: (i) Action Completion, (ii) Action Order Understanding, and (iii) Action Regularity Understanding. These tasks help a model understand the temporal consistency of actions across videos. To the best of our knowledge, this is the first attempt to explicitly explore temporal consistency for point supervision action localization. Extensive experimental results on four benchmark datasets demonstrate the effectiveness of the proposed method compared to several state-of-the-art approaches.

Problem

• Temporal Action Localization
• Point Supervision
• Temporal Consistency
• Weakly-Supervised Learning
• Action Understanding

Innovation

• Temporal Consistency
• Point-Supervised Learning
• Temporal Action Localization
• Self-Supervised Learning
• Multi-Task Learning