{"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/one-shot-action-recognition-towards-novel","title":"One-shot action recognition in challenging therapy scenarios","arxiv_id":"2102.08997","date":"2021-02-17","proceeding":null,"authors":["Alberto Sabater","Laura Santos","Jose Santos-Victor","Alexandre Bernardino","Luis Montesano","Ana C. Murillo"],"abstract":"One-shot action recognition aims to recognize new action categories from a single reference example, typically referred to as the anchor example. This work presents a novel approach for one-shot action recognition in the wild that computes motion representations robust to variable kinematic conditions. One-shot action recognition is then performed by evaluating anchor and target motion representations. We also develop a set of complementary steps that boost the action recognition performance in the most challenging scenarios. Our approach is evaluated on the public NTU-120 one-shot action recognition benchmark, outperforming previous action recognition models. Besides, we evaluate our framework on a real use-case of therapy with autistic people. These recordings are particularly challenging due to high-level artifacts from the patient motion. Our results provide not only quantitative but also online qualitative measures, essential for the patient evaluation and monitoring during the actual therapy.","url_abs":"https://arxiv.org/abs/2102.08997v4","url_pdf":"https://arxiv.org/pdf/2102.08997v4.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":"one-shot-action-recognition-towards-novel","repo_url":"https://github.com/AlbertoSabater/Skeleton-based-One-shot-Action-Recognition","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"one-shot-3d-action-recognition","task_name":"One-Shot 3D Action Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/one-shot-3d-action-recognition-on-ntu-rgbd","task":"One-Shot 3D Action Recognition","dataset":"NTU RGB+D 120","model":"TCN_OneShot","rank_in_archive_order":6,"of":10,"metrics":{"Accuracy":"46.5%"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2102.08997","atlas_url":"https://app.syntology.ai/?focus=2102.08997","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}