{"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/neural-graph-matching-networks-for-fewshot-3d","title":"Neural Graph Matching Networks for Fewshot 3D Action Recognition","arxiv_id":null,"date":"2018-09-01","proceeding":"ECCV 2018 9","authors":["Michelle Guo","Edward Chou","De-An Huang","Shuran Song","Serena Yeung","Li Fei-Fei"],"abstract":"We propose Neural Graph Matching (NGM) Networks, a novel framework that can learn to recognize a previous unseen 3D action class with only a few examples. We achieve this by leveraging the inherent structure of 3D data through a graphical representation. This allows us to modularize our model and lead to strong data-efficiency in few-shot learning. More specifically, NGM Networks jointly learn a graph generator and a graph matching metric function in a end-to-end fashion to directly optimize the few-shot learning objective. We evaluate NGM on two 3D action recognition datasets, CAD-120 and PiGraphs, and show that learning to generate and match graphs both lead to significant improvement of few-shot 3D action recognition over the holistic baselines.","url_abs":"http://openaccess.thecvf.com/content_ECCV_2018/html/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_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":[],"tasks":[{"task_slug":"3d-human-action-recognition","task_name":"3D Action Recognition"},{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"graph-matching","task_name":"Graph Matching"},{"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-cad-120","task":"Skeleton Based Action Recognition","dataset":"CAD-120","model":"NGM (5-shot)","rank_in_archive_order":1,"of":8,"metrics":{"Accuracy":"91.1%"},"uses_additional_data":false},{"leaderboard":"/sota/skeleton-based-action-recognition-on-cad-120","task":"Skeleton Based Action Recognition","dataset":"CAD-120","model":"NGM w/o Edges  (5-shot)","rank_in_archive_order":5,"of":8,"metrics":{"Accuracy":"85.0%"},"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}