{"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/construct-dynamic-graphs-for-hand-gesture","title":"Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention","arxiv_id":"1907.08871","date":"2019-07-20","proceeding":null,"authors":["Yuxiao Chen","Long Zhao","Xi Peng","Jianbo Yuan","Dimitris N. Metaxas"],"abstract":"We propose a Dynamic Graph-Based Spatial-Temporal Attention (DG-STA) method for hand gesture recognition. The key idea is to first construct a fully-connected graph from a hand skeleton, where the node features and edges are then automatically learned via a self-attention mechanism that performs in both spatial and temporal domains. We further propose to leverage the spatial-temporal cues of joint positions to guarantee robust recognition in challenging conditions. In addition, a novel spatial-temporal mask is applied to significantly cut down the computational cost by 99%. We carry out extensive experiments on benchmarks (DHG-14/28 and SHREC'17) and prove the superior performance of our method compared with the state-of-the-art methods. The source code can be found at https://github.com/yuxiaochen1103/DG-STA.","url_abs":"https://arxiv.org/abs/1907.08871v1","url_pdf":"https://arxiv.org/pdf/1907.08871v1.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":"construct-dynamic-graphs-for-hand-gesture","repo_url":"https://github.com/yuxiaochen1103/DG-STA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"},{"task_slug":"hand-gesture-recognition","task_name":"Hand Gesture Recognition"},{"task_slug":"hand-gesture-recognition-1","task_name":"Hand-Gesture Recognition"},{"task_slug":"skeleton-based-action-recognition","task_name":"Skeleton Based Action Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hand-gesture-recognition-on-dhg-14","task":"Hand Gesture Recognition","dataset":"DHG-14","model":"DG-STA","rank_in_archive_order":6,"of":13,"metrics":{"Accuracy":"91.9"},"uses_additional_data":false},{"leaderboard":"/sota/hand-gesture-recognition-on-dhg-28","task":"Hand Gesture Recognition","dataset":"DHG-28","model":"DG-STA","rank_in_archive_order":7,"of":9,"metrics":{"Accuracy":"88"},"uses_additional_data":false},{"leaderboard":"/sota/hand-gesture-recognition-on-shrec-2017","task":"Hand Gesture Recognition","dataset":"SHREC 2017","model":"DG-STA","rank_in_archive_order":4,"of":4,"metrics":{"14 Gestures Accuracy":"94.4","28 Gestures Accuracy":"90.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1907.08871","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}