{"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/hacs-human-action-clips-and-segments-dataset","title":"HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization","arxiv_id":"1712.09374","date":"2017-12-26","proceeding":"ICCV 2019 10","authors":["Hang Zhao","Antonio Torralba","Lorenzo Torresani","Zhicheng Yan"],"abstract":"This paper presents a new large-scale dataset for recognition and temporal localization of human actions collected from Web videos. We refer to it as HACS (Human Action Clips and Segments). We leverage both consensus and disagreement among visual classifiers to automatically mine candidate short clips from unlabeled videos, which are subsequently validated by human annotators. The resulting dataset is dubbed HACS Clips. Through a separate process we also collect annotations defining action segment boundaries. This resulting dataset is called HACS Segments. Overall, HACS Clips consists of 1.5M annotated clips sampled from 504K untrimmed videos, and HACS Seg-ments contains 139K action segments densely annotatedin 50K untrimmed videos spanning 200 action categories. HACS Clips contains more labeled examples than any existing video benchmark. This renders our dataset both a large scale action recognition benchmark and an excellent source for spatiotemporal feature learning. In our transferlearning experiments on three target datasets, HACS Clips outperforms Kinetics-600, Moments-In-Time and Sports1Mas a pretraining source. On HACS Segments, we evaluate state-of-the-art methods of action proposal generation and action localization, and highlight the new challenges posed by our dense temporal annotations.","url_abs":"https://arxiv.org/abs/1712.09374v3","url_pdf":"https://arxiv.org/pdf/1712.09374v3.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":"hacs-human-action-clips-and-segments-dataset","repo_url":"https://github.com/hangzhaomit/HACS-dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"hacs-human-action-clips-and-segments-dataset","repo_url":"https://github.com/musicalOffering/sola","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"action-classification","task_name":"Action Classification"},{"task_slug":"action-localization","task_name":"Action Localization"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"},{"task_slug":"temporal-localization","task_name":"Temporal Localization"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[{"slug":"hacs","name":"HACS","full_name":"Human Action Clips and Segments"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/temporal-action-localization-on-hacs","task":"Temporal Action Localization","dataset":"HACS","model":"SSN","rank_in_archive_order":12,"of":12,"metrics":{"Average-mAP":"18.97","mAP@0.5":"28.82","mAP@0.75":"18.80","mAP@0.95":"5.32"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1712.09374","atlas_url":"https://app.syntology.ai/?focus=1712.09374","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.09374"}},"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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