{"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/multi-stage-rgb-based-transfer-learning","title":"Multi-stage RGB-based Transfer Learning Pipeline for Hand Activity Recognition","arxiv_id":null,"date":"2022-02-08","proceeding":"17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2022 2","authors":["Yasser Boutaleb","Catherine Soladie","Nam-Duong Duong Jérôme Royan","Renaud Seguier"],"abstract":"First-person hand activity recognition is a challenging task, especially when not enough data are available. In this paper, we tackle this challenge by proposing a new low-cost multi-stage learning pipeline for first-person RGB-based hand activity recognition on a limited amount of data. For a given RGB image activity sequence, in the first stage, the regions of interest are extracted using a pre-trained neural network (NN). Then, in the second stage, high-level spatial features are extracted using pre-trained deep NN. In the third stage, the temporal dependencies are learned. Finally, in the last stage, a hand activity sequence classifier is learned, using a post-fusion strategy, which is applied to the previously learned temporal dependencies. The experiments evaluated on two real-world data sets shows that our pipeline achieves the state-of-the-art. Moreover, it shows that the proposed pipeline achieves good results on limited data.","url_abs":"https://www.scitepress.org/Link.aspx?doi=10.5220/0010856200003124","url_pdf":"https://www.scitepress.org/Link.aspx?doi=10.5220/0010856200003124","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":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/activity-recognition-on-first-person-hand","task":"Activity Recognition","dataset":"First-Person Hand Action Benchmark","model":"Boutaleb et al.","rank_in_archive_order":1,"of":1,"metrics":{"1:1 Accuracy":"97.91"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}