{"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/dirv-dense-interaction-region-voting-for-end","title":"DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection","arxiv_id":"2010.01005","date":"2020-10-02","proceeding":null,"authors":["Hao-Shu Fang","Yichen Xie","Dian Shao","Cewu Lu"],"abstract":"Recent years, human-object interaction (HOI) detection has achieved impressive advances. However, conventional two-stage methods are usually slow in inference. On the other hand, existing one-stage methods mainly focus on the union regions of interactions, which introduce unnecessary visual information as disturbances to HOI detection. To tackle the problems above, we propose a novel one-stage HOI detection approach DIRV in this paper, based on a new concept called interaction region for the HOI problem. Unlike previous methods, our approach concentrates on the densely sampled interaction regions across different scales for each human-object pair, so as to capture the subtle visual features that is most essential to the interaction. Moreover, in order to compensate for the detection flaws of a single interaction region, we introduce a novel voting strategy that makes full use of those overlapped interaction regions in place of conventional Non-Maximal Suppression (NMS). Extensive experiments on two popular benchmarks: V-COCO and HICO-DET show that our approach outperforms existing state-of-the-arts by a large margin with the highest inference speed and lightest network architecture. We achieved 56.1 mAP on V-COCO without addtional input. Our code is publicly available at: https://github.com/MVIG-SJTU/DIRV","url_abs":"https://arxiv.org/abs/2010.01005v2","url_pdf":"https://arxiv.org/pdf/2010.01005v2.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":"dirv-dense-interaction-region-voting-for-end","repo_url":"https://github.com/MVIG-SJTU/DIRV","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"human-object-interaction-detection","task_name":"Human-Object Interaction Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/human-object-interaction-detection-on-hico","task":"Human-Object Interaction Detection","dataset":"HICO-DET","model":"DIRV","rank_in_archive_order":45,"of":55,"metrics":{"Time Per Frame (ms)":"68","mAP":"21.81"},"uses_additional_data":false},{"leaderboard":"/sota/human-object-interaction-detection-on-v-coco","task":"Human-Object Interaction Detection","dataset":"V-COCO","model":"DIRV","rank_in_archive_order":15,"of":34,"metrics":{"AP(S1)":"56.1","Time Per Frame(ms)":"68"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2010.01005","atlas_url":"https://app.syntology.ai/?focus=2010.01005","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.01005"}},"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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