{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hr-pro-point-supervised-temporal-action","title":"HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation","arxiv_id":"2308.12608","date":"2023-08-24","proceeding":null,"authors":["Huaxin Zhang","Xiang Wang","Xiaohao Xu","Zhiwu Qing","Changxin Gao","Nong Sang"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2308.12608v3","url_pdf":"https://arxiv.org/pdf/2308.12608v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hr-pro-point-supervised-temporal-action","repo_url":"https://github.com/pipixin321/hr-pro","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"action-localization","task_name":"Action Localization"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"},{"task_slug":"weakly-supervised-action-localization","task_name":"Weakly Supervised Action Localization"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/weakly-supervised-action-localization-on-6","task":"Weakly Supervised Action Localization","dataset":"BEOID","model":"HR-Pro","rank_in_archive_order":1,"of":5,"metrics":{"mAP@0.1:0.7":"59.4","mAP@0.5":"55.3"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-action-localization-on-gtea","task":"Weakly Supervised Action Localization","dataset":"GTEA","model":"HR-Pro","rank_in_archive_order":2,"of":6,"metrics":{"mAP@0.1:0.7":"47.3","mAP@0.5":"37.3"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-action-localization-on","task":"Weakly Supervised Action Localization","dataset":"THUMOS 2014","model":"HR-Pro","rank_in_archive_order":1,"of":30,"metrics":{"mAP@0.1:0.5":"71.6","mAP@0.1:0.7":"60.3","mAP@0.5":"52.2"},"uses_additional_data":true},{"leaderboard":"/sota/weakly-supervised-action-localization-on-5","task":"Weakly Supervised Action Localization","dataset":"THUMOS14","model":"HR-Pro","rank_in_archive_order":1,"of":12,"metrics":{"avg-mAP (0.1-0.5)":"71.6","avg-mAP (0.1:0.7)":"60.3","avg-mAP (0.3-0.7)":"51.1"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2308.12608","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}