{"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/end-to-end-spatio-temporal-action","title":"End-to-End Spatio-Temporal Action Localisation with Video Transformers","arxiv_id":"2304.12160","date":"2023-04-24","proceeding":"CVPR 2024 1","authors":["Alexey Gritsenko","Xuehan Xiong","Josip Djolonga","Mostafa Dehghani","Chen Sun","Mario Lučić","Cordelia Schmid","Anurag Arnab"],"abstract":"The most performant spatio-temporal action localisation models use external person proposals and complex external memory banks. We propose a fully end-to-end, purely-transformer based model that directly ingests an input video, and outputs tubelets -- a sequence of bounding boxes and the action classes at each frame. Our flexible model can be trained with either sparse bounding-box supervision on individual frames, or full tubelet annotations. And in both cases, it predicts coherent tubelets as the output. Moreover, our end-to-end model requires no additional pre-processing in the form of proposals, or post-processing in terms of non-maximal suppression. We perform extensive ablation experiments, and significantly advance the state-of-the-art results on four different spatio-temporal action localisation benchmarks with both sparse keyframes and full tubelet annotations.","url_abs":"https://arxiv.org/abs/2304.12160v1","url_pdf":"https://arxiv.org/pdf/2304.12160v1.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-detection","task_name":"Action Detection"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"spatio-temporal-action-localization","task_name":"Spatio-Temporal Action Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/action-detection-on-ucf101-24","task":"Action Detection","dataset":"UCF101-24","model":"STAR/L","rank_in_archive_order":1,"of":19,"metrics":{"Frame-mAP 0.5":"90.3","Video-mAP 0.2":"88.0","Video-mAP 0.5":"71.8"},"uses_additional_data":true},{"leaderboard":"/sota/action-recognition-in-videos-on-ava-v21","task":"Action Recognition","dataset":"AVA v2.1","model":"STAR/L","rank_in_archive_order":1,"of":15,"metrics":{"mAP (Val)":"41.7"},"uses_additional_data":true},{"leaderboard":"/sota/action-recognition-on-ava-v2-2","task":"Action Recognition","dataset":"AVA v2.2","model":"STAR/L","rank_in_archive_order":4,"of":38,"metrics":{"mAP":"41.7"},"uses_additional_data":true},{"leaderboard":"/sota/spatio-temporal-action-localization-on-ava","task":"Spatio-Temporal Action Localization","dataset":"AVA-Kinetics","model":"STAR/L","rank_in_archive_order":2,"of":7,"metrics":{"val mAP":"41.7"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2304.12160","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}