{"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/weakly-supervised-action-selection-learning","title":"Weakly Supervised Action Selection Learning in Video","arxiv_id":"2105.02439","date":"2021-05-06","proceeding":"CVPR 2021 1","authors":["Junwei Ma","Satya Krishna Gorti","Maksims Volkovs","Guangwei Yu"],"abstract":"Localizing actions in video is a core task in computer vision. The weakly supervised temporal localization problem investigates whether this task can be adequately solved with only video-level labels, significantly reducing the amount of expensive and error-prone annotation that is required. A common approach is to train a frame-level classifier where frames with the highest class probability are selected to make a video-level prediction. Frame level activations are then used for localization. However, the absence of frame-level annotations cause the classifier to impart class bias on every frame. To address this, we propose the Action Selection Learning (ASL) approach to capture the general concept of action, a property we refer to as \"actionness\". Under ASL, the model is trained with a novel class-agnostic task to predict which frames will be selected by the classifier. Empirically, we show that ASL outperforms leading baselines on two popular benchmarks THUMOS-14 and ActivityNet-1.2, with 10.3% and 5.7% relative improvement respectively. We further analyze the properties of ASL and demonstrate the importance of actionness. Full code for this work is available here: https://github.com/layer6ai-labs/ASL.","url_abs":"https://arxiv.org/abs/2105.02439v1","url_pdf":"https://arxiv.org/pdf/2105.02439v1.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":"weakly-supervised-action-selection-learning","repo_url":"https://github.com/layer6ai-labs/ASL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"temporal-localization","task_name":"Temporal Localization"},{"task_slug":"weakly-supervised-action-localization","task_name":"Weakly Supervised Action Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/weakly-supervised-action-localization-on-2","task":"Weakly Supervised Action Localization","dataset":"ActivityNet-1.2","model":"ASL","rank_in_archive_order":10,"of":19,"metrics":{"Mean mAP":"25.8","mAP@0.5":"40.2"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-action-localization-on-7","task":"Weakly Supervised Action Localization","dataset":"FineAction","model":"ASL","rank_in_archive_order":4,"of":4,"metrics":{"mAP":"3.30","mAP IOU@0.5":"2.68","mAP IOU@0.75":"0.81","mAP IOU@0.95":"3.30"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2105.02439","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.02439"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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