{"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/modeling-multi-label-action-dependencies-for","title":"Modeling Multi-Label Action Dependencies for Temporal Action Localization","arxiv_id":"2103.03027","date":"2021-03-04","proceeding":"CVPR 2021 1","authors":["Praveen Tirupattur","Kevin Duarte","Yogesh Rawat","Mubarak Shah"],"abstract":"Real-world videos contain many complex actions with inherent relationships between action classes. In this work, we propose an attention-based architecture that models these action relationships for the task of temporal action localization in untrimmed videos. As opposed to previous works that leverage video-level co-occurrence of actions, we distinguish the relationships between actions that occur at the same time-step and actions that occur at different time-steps (i.e. those which precede or follow each other). We define these distinct relationships as action dependencies. We propose to improve action localization performance by modeling these action dependencies in a novel attention-based Multi-Label Action Dependency (MLAD)layer. The MLAD layer consists of two branches: a Co-occurrence Dependency Branch and a Temporal Dependency Branch to model co-occurrence action dependencies and temporal action dependencies, respectively. We observe that existing metrics used for multi-label classification do not explicitly measure how well action dependencies are modeled, therefore, we propose novel metrics that consider both co-occurrence and temporal dependencies between action classes. Through empirical evaluation and extensive analysis, we show improved performance over state-of-the-art methods on multi-label action localization benchmarks(MultiTHUMOS and Charades) in terms of f-mAP and our proposed metric.","url_abs":"https://arxiv.org/abs/2103.03027v3","url_pdf":"https://arxiv.org/pdf/2103.03027v3.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":"modeling-multi-label-action-dependencies-for","repo_url":"https://github.com/ptirupat/MLAD","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"action-detection","task_name":"Action Detection"},{"task_slug":"action-localization","task_name":"Action Localization"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-detection-on-charades","task":"Action Detection","dataset":"Charades","model":"MLAD (RGB + Flow)","rank_in_archive_order":11,"of":16,"metrics":{"mAP":"23.7"},"uses_additional_data":false},{"leaderboard":"/sota/action-detection-on-multi-thumos","task":"Action Detection","dataset":"Multi-THUMOS","model":"MLAD","rank_in_archive_order":1,"of":8,"metrics":{"mAP":"51.5"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-action-localization-on-multithumos-1","task":"Temporal Action Localization","dataset":"MultiTHUMOS","model":"MLAD","rank_in_archive_order":8,"of":8,"metrics":{"Average mAP":"14.2"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2103.03027","atlas_url":"https://app.syntology.ai/?focus=2103.03027","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.03027"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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