{"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/infogcn-representation-learning-for-human","title":"InfoGCN: Representation Learning for Human Skeleton-Based Action Recognition","arxiv_id":null,"date":"2022-01-01","proceeding":"CVPR 2022 1","authors":["Hyung-gun Chi","Myoung Hoon Ha","Seunggeun Chi","Sang Wan Lee","QiXing Huang","Karthik Ramani"],"abstract":"    Human skeleton-based action recognition offers a valuable means to understand the intricacies of human behavior because it can handle the complex relationships between physical constraints and intention. Although several studies have focused on encoding a skeleton, less attention has been paid to embed this information into the latent representations of human action. InfoGCN proposes a learning framework for action recognition combining a novel learning objective and an encoding method. First, we design an information bottleneck-based learning objective to guide the model to learn informative but compact latent representations. To provide discriminative information for classifying action, we introduce attention-based graph convolution that captures the context-dependent intrinsic topology of human action. In addition, we present a multi-modal representation of the skeleton using the relative position of joints, designed to provide complementary spatial information for joints. InfoGCN surpasses the known state-of-the-art on multiple skeleton-based action recognition benchmarks with the accuracy of 93.0% on NTU RGB+D 60 cross-subject split, 89.8% on NTU RGB+D 120 cross-subject split, and 97.0% on NW-UCLA.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2022/html/Chi_InfoGCN_Representation_Learning_for_Human_Skeleton-Based_Action_Recognition_CVPR_2022_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2022/papers/Chi_InfoGCN_Representation_Learning_for_Human_Skeleton-Based_Action_Recognition_CVPR_2022_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":"infogcn-representation-learning-for-human","repo_url":"https://github.com/stnoah1/infogcn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"skeleton-based-action-recognition","task_name":"Skeleton Based Action Recognition"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/skeleton-based-action-recognition-on-n-ucla","task":"Skeleton Based Action Recognition","dataset":"N-UCLA","model":"InfoGCN","rank_in_archive_order":9,"of":25,"metrics":{"Accuracy":"97.0"},"uses_additional_data":false},{"leaderboard":"/sota/skeleton-based-action-recognition-on-ntu-rgbd","task":"Skeleton Based Action Recognition","dataset":"NTU RGB+D","model":"InfoGCN","rank_in_archive_order":22,"of":135,"metrics":{"Accuracy (CS)":"93.0","Accuracy (CV)":"97.1","Ensembled Modalities":"6"},"uses_additional_data":false},{"leaderboard":"/sota/skeleton-based-action-recognition-on-ntu-rgbd-1","task":"Skeleton Based Action Recognition","dataset":"NTU RGB+D 120","model":"InfoGCN","rank_in_archive_order":15,"of":83,"metrics":{"Accuracy (Cross-Setup)":"91.2","Accuracy (Cross-Subject)":"89.8","Ensembled Modalities":"6"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}