{"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/temporal-deformable-residual-networks-for","title":"Temporal Deformable Residual Networks for Action Segmentation in Videos","arxiv_id":null,"date":"2018-06-01","proceeding":"CVPR 2018 6","authors":["Peng Lei","Sinisa Todorovic"],"abstract":"This paper is about temporal segmentation of human actions in videos. We introduce a new model -- temporal deformable residual network (TDRN) -- aimed at  analyzing video intervals at multiple temporal scales for labeling video frames.  Our TDRN computes two parallel temporal streams: i) Residual stream that analyzes video information at its full temporal  resolution, and ii) Pooling/unpooling stream that captures long-range video information at different scales. The former  facilitates local, fine-scale action segmentation, and the latter uses multiscale context for improving accuracy of frame classification.  These two streams are computed by a set of temporal residual modules with deformable convolutions, and fused by temporal residuals at the full video resolution. Our evaluation on the University of Dundee 50 Salads, Georgia Tech  Egocentric Activities, and JHU-ISI Gesture and Skill Assessment Working Set demonstrates that TDRN outperforms the state of the art  in frame-wise segmentation accuracy, segmental edit score, and segmental overlap F1 score.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2018/html/Lei_Temporal_Deformable_Residual_CVPR_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2018/papers/Lei_Temporal_Deformable_Residual_CVPR_2018_paper.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":[],"tasks":[{"task_slug":"action-segmentation","task_name":"Action Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-segmentation-on-gtea-1","task":"Action Segmentation","dataset":"GTEA","model":"TDRN","rank_in_archive_order":26,"of":28,"metrics":{"Acc":"70.1","Edit":" 74.1","F1@10%":"79.2","F1@25%":"74.4","F1@50%":"62.7"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}