{"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/domain-and-view-point-agnostic-hand-action","title":"Domain and View-point Agnostic Hand Action Recognition","arxiv_id":"2103.02303","date":"2021-03-03","proceeding":null,"authors":["Alberto Sabater","Iñigo Alonso","Luis Montesano","Ana C. Murillo"],"abstract":"Hand action recognition is a special case of action recognition with applications in human-robot interaction, virtual reality or life-logging systems. Building action classifiers able to work for such heterogeneous action domains is very challenging. There are very subtle changes across different actions from a given application but also large variations across domains (e.g. virtual reality vs life-logging). This work introduces a novel skeleton-based hand motion representation model that tackles this problem. The framework we propose is agnostic to the application domain or camera recording view-point. When working on a single domain (intra-domain action classification) our approach performs better or similar to current state-of-the-art methods on well-known hand action recognition benchmarks. And, more importantly, when performing hand action recognition for action domains and camera perspectives which our approach has not been trained for (cross-domain action classification), our proposed framework achieves comparable performance to intra-domain state-of-the-art methods. These experiments show the robustness and generalization capabilities of our framework.","url_abs":"https://arxiv.org/abs/2103.02303v3","url_pdf":"https://arxiv.org/pdf/2103.02303v3.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":"domain-and-view-point-agnostic-hand-action","repo_url":"https://github.com/AlbertoSabater/Domain-and-View-point-Agnostic-Hand-Action-Recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"action-classification","task_name":"Action Classification"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"hand-gesture-recognition","task_name":"Hand Gesture Recognition"},{"task_slug":"skeleton-based-action-recognition","task_name":"Skeleton Based Action Recognition"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/skeleton-based-action-recognition-on-first","task":"Skeleton Based Action Recognition","dataset":"First-Person Hand Action Benchmark","model":"TCN-Summ","rank_in_archive_order":1,"of":4,"metrics":{"1:1 Accuracy":"95.93","1:3 Accuracy":"92.9","3:1 Accuracy":"96.76","Cross-person Accuracy":"88.70"},"uses_additional_data":false},{"leaderboard":"/sota/skeleton-based-action-recognition-on-shrec","task":"Skeleton Based Action Recognition","dataset":"SHREC 2017 track on 3D Hand Gesture Recognition","model":"TCN-Summ","rank_in_archive_order":5,"of":7,"metrics":{"14 gestures accuracy":"93.57","28 gestures accuracy":"91.43"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2103.02303","atlas_url":"https://app.syntology.ai/?focus=2103.02303","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}