{"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/skeleton-dml-deep-metric-learning-for","title":"Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action Recognition","arxiv_id":"2012.13823","date":"2020-12-26","proceeding":null,"authors":["Raphael Memmesheimer","Simon Häring","Nick Theisen","Dietrich Paulus"],"abstract":"One-shot action recognition allows the recognition of human-performed actions with only a single training example. This can influence human-robot-interaction positively by enabling the robot to react to previously unseen behaviour. We formulate the one-shot action recognition problem as a deep metric learning problem and propose a novel image-based skeleton representation that performs well in a metric learning setting. Therefore, we train a model that projects the image representations into an embedding space. In embedding space the similar actions have a low euclidean distance while dissimilar actions have a higher distance. The one-shot action recognition problem becomes a nearest-neighbor search in a set of activity reference samples. We evaluate the performance of our proposed representation against a variety of other skeleton-based image representations. In addition, we present an ablation study that shows the influence of different embedding vector sizes, losses and augmentation. Our approach lifts the state-of-the-art by 3.3% for the one-shot action recognition protocol on the NTU RGB+D 120 dataset under a comparable training setup. With additional augmentation our result improved over 7.7%.","url_abs":"https://arxiv.org/abs/2012.13823v2","url_pdf":"https://arxiv.org/pdf/2012.13823v2.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":"skeleton-dml-deep-metric-learning-for","repo_url":"https://github.com/raphaelmemmesheimer/skeleton-dml","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":"one-shot-3d-action-recognition","task_name":"One-Shot 3D Action Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/one-shot-3d-action-recognition-on-ntu-rgbd","task":"One-Shot 3D Action Recognition","dataset":"NTU RGB+D 120","model":"Skeleton-DML","rank_in_archive_order":4,"of":10,"metrics":{"Accuracy":"54.2%"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2012.13823","atlas_url":"https://app.syntology.ai/?focus=2012.13823","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}