{"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/data-free-class-incremental-hand-gesture","title":"Data-Free Class-Incremental Hand Gesture Recognition","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Shubhra Aich","Jesus Ruiz-Santaquiteria","Zhenyu Lu","Prachi Garg","K J Joseph","Alvaro Fernandez Garcia","Vineeth N Balasubramanian","Kenrick Kin","Chengde Wan","Necati Cihan Camgoz","Shugao Ma","Fernando de la Torre"],"abstract":"    This paper investigates data-free class-incremental learning (DFCIL)  for hand gesture recognition from 3D skeleton sequences.  In this class-incremental learning (CIL) setting, while incrementally  registering the new classes, we do not have access to the training  samples (i.e. data-free) of the already known classes due to privacy.  Existing DFCIL methods primarily focus on various forms of  knowledge distillation for model inversion to mitigate  catastrophic forgetting. Unlike SOTA methods,  we delve deeper into the choice of the best samples for inversion.  Inspired by the well-grounded theory of max-margin classification,  we find that the best samples tend to lie close to the approximate  decision boundary within a reasonable margin. To this end,  we propose BOAT-MI -- a simple and effective boundary-aware prototypical  sampling mechanism for model inversion for DFCIL.  Our sampling scheme outperforms SOTA methods significantly  on two 3D skeleton gesture datasets, the publicly available  SHREC 2017, and EgoGesture3D -- which we extract from a publicly  available RGBD dataset. Both our codebase and the EgoGesture3D  skeleton dataset are publicly available: https://github.com/humansensinglab/dfcil-hgr    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Aich_Data-Free_Class-Incremental_Hand_Gesture_Recognition_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Aich_Data-Free_Class-Incremental_Hand_Gesture_Recognition_ICCV_2023_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":[{"paper_slug":"data-free-class-incremental-hand-gesture","repo_url":"https://github.com/humansensinglab/dfcil-hgr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"class-incremental-learning","task_name":"Class Incremental Learning"},{"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":"incremental-learning","task_name":"Incremental Learning"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"},{"task_slug":"class-incremental-learning-1","task_name":"class-incremental learning"}],"methods":[{"method_slug":"knowledge-distillation","method_name":"Knowledge Distillation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}