{"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/colar-effective-and-efficient-online-action","title":"Colar: Effective and Efficient Online Action Detection by Consulting Exemplars","arxiv_id":"2203.01057","date":"2022-03-02","proceeding":"CVPR 2022 1","authors":["Le Yang","Junwei Han","Dingwen Zhang"],"abstract":"Online action detection has attracted increasing research interests in recent years. Current works model historical dependencies and anticipate the future to perceive the action evolution within a video segment and improve the detection accuracy. However, the existing paradigm ignores category-level modeling and does not pay sufficient attention to efficiency. Considering a category, its representative frames exhibit various characteristics. Thus, the category-level modeling can provide complimentary guidance to the temporal dependencies modeling. This paper develops an effective exemplar-consultation mechanism that first measures the similarity between a frame and exemplary frames, and then aggregates exemplary features based on the similarity weights. This is also an efficient mechanism, as both similarity measurement and feature aggregation require limited computations. Based on the exemplar-consultation mechanism, the long-term dependencies can be captured by regarding historical frames as exemplars, while the category-level modeling can be achieved by regarding representative frames from a category as exemplars. Due to the complementarity from the category-level modeling, our method employs a lightweight architecture but achieves new high performance on three benchmarks. In addition, using a spatio-temporal network to tackle video frames, our method makes a good trade-off between effectiveness and efficiency. Code is available at https://github.com/VividLe/Online-Action-Detection.","url_abs":"https://arxiv.org/abs/2203.01057v2","url_pdf":"https://arxiv.org/pdf/2203.01057v2.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":"colar-effective-and-efficient-online-action","repo_url":"https://github.com/vividle/online-action-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"action-detection","task_name":"Action Detection"},{"task_slug":"online-action-detection","task_name":"Online Action Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/online-action-detection-on-thumos-14","task":"Online Action Detection","dataset":"THUMOS'14","model":"Colar","rank_in_archive_order":6,"of":15,"metrics":{"mAP":"66.9"},"uses_additional_data":false},{"leaderboard":"/sota/online-action-detection-on-thumos-14","task":"Online Action Detection","dataset":"THUMOS'14","model":"Colar(RGB only)","rank_in_archive_order":14,"of":15,"metrics":{"mAP":"58.6"},"uses_additional_data":false},{"leaderboard":"/sota/online-action-detection-on-tvseries","task":"Online Action Detection","dataset":"TVSeries","model":"Colar","rank_in_archive_order":6,"of":13,"metrics":{"mCAP":"88.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.01057","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01057"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/vividle/online-action-detection","reach":{"status":"ok"}},{"provenance":"deterministic:regex_extraction","url":"https://github.com/VividLe/Online-Action-Detection","reach":{"status":"ok"}}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"551d6bb4299c6d84","entry":"Colar_static","repo":"VividLe/Online-Action-Detection","repo_kind":"official","path":"model/ColarModel.py","file_url":"https://github.com/VividLe/Online-Action-Detection/blob/HEAD/model/ColarModel.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"551d6bb4299c6d84"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}