{"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/dance-with-flow-two-in-one-stream-action","title":"Dance with Flow: Two-in-One Stream Action Detection","arxiv_id":"1904.00696","date":"2019-04-01","proceeding":"CVPR 2019 6","authors":["Jiaojiao Zhao","Cees G. M. Snoek"],"abstract":"The goal of this paper is to detect the spatio-temporal extent of an action. The two-stream detection network based on RGB and flow provides state-of-the-art accuracy at the expense of a large model-size and heavy computation. We propose to embed RGB and optical-flow into a single two-in-one stream network with new layers. A motion condition layer extracts motion information from flow images, which is leveraged by the motion modulation layer to generate transformation parameters for modulating the low-level RGB features. The method is easily embedded in existing appearance- or two-stream action detection networks, and trained end-to-end. Experiments demonstrate that leveraging the motion condition to modulate RGB features improves detection accuracy. With only half the computation and parameters of the state-of-the-art two-stream methods, our two-in-one stream still achieves impressive results on UCF101-24, UCFSports and J-HMDB.","url_abs":"https://arxiv.org/abs/1904.00696v3","url_pdf":"https://arxiv.org/pdf/1904.00696v3.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":"dance-with-flow-two-in-one-stream-action","repo_url":"https://github.com/jiaozizhao/Two-in-One-ActionDetection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"action-detection","task_name":"Action Detection"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-detection-on-j-hmdb","task":"Action Detection","dataset":"J-HMDB","model":"Two-in-one Two Stream","rank_in_archive_order":16,"of":18,"metrics":{"Video-mAP 0.5":"74.74"},"uses_additional_data":false},{"leaderboard":"/sota/action-detection-on-j-hmdb","task":"Action Detection","dataset":"J-HMDB","model":"Two-in-one","rank_in_archive_order":17,"of":18,"metrics":{"Video-mAP 0.5":"57.96"},"uses_additional_data":false},{"leaderboard":"/sota/action-detection-on-ucf-sports","task":"Action Detection","dataset":"UCF Sports","model":"Two-in-one Two Stream","rank_in_archive_order":6,"of":7,"metrics":{"Video-mAP 0.5":"96.52"},"uses_additional_data":false},{"leaderboard":"/sota/action-detection-on-ucf-sports","task":"Action Detection","dataset":"UCF Sports","model":"Two-in-one","rank_in_archive_order":7,"of":7,"metrics":{"Video-mAP 0.5":"92.74"},"uses_additional_data":false},{"leaderboard":"/sota/action-detection-on-ucf101-24","task":"Action Detection","dataset":"UCF101-24","model":"Two-in-one Two Stream","rank_in_archive_order":17,"of":19,"metrics":{"Video-mAP 0.2":"78.48","Video-mAP 0.5":"50.30"},"uses_additional_data":false},{"leaderboard":"/sota/action-detection-on-ucf101-24","task":"Action Detection","dataset":"UCF101-24","model":"Two-in-one","rank_in_archive_order":18,"of":19,"metrics":{"Video-mAP 0.2":"75.48","Video-mAP 0.5":"48.31"},"uses_additional_data":false},{"leaderboard":"/sota/action-recognition-in-videos-on-ucf101","task":"Action Recognition","dataset":"UCF101","model":"two-in-one two stream","rank_in_archive_order":65,"of":91,"metrics":{"3-fold Accuracy":"92"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.00696","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}