{"id":3883,"date":"2021-08-12T12:27:41","date_gmt":"2021-08-12T11:27:41","guid":{"rendered":"https:\/\/www.ceessnoek.info\/?p=3883"},"modified":"2021-08-12T12:27:43","modified_gmt":"2021-08-12T11:27:43","slug":"mm-2021-skeleton-contrastive-3d-action-representation-learning","status":"publish","type":"post","link":"https:\/\/www.ceessnoek.info\/index.php\/mm-2021-skeleton-contrastive-3d-action-representation-learning\/","title":{"rendered":"MM 2021: Skeleton-Contrastive 3D Action Representation Learning"},"content":{"rendered":"\n

The ACM Multimedia 2021 paper Skeleton-Contrastive 3D Action Representation Learning by Fida Thoker<\/a>, Hazel Doughty<\/a> and Cees Snoek is now available<\/a>. This paper strives for self-supervised learning of a feature space suitable for skeleton-based action recognition. Our proposal is built upon learning invariances to input skeleton representations and various skeleton augmentations via a noise contrastive estimation. In particular, we propose inter-skeleton contrastive learning, which learns from multiple different input skeleton representations in a cross-contrastive manner. In addition, we contribute several skeleton-specific spatial and temporal augmentations which further encourage the model to learn the spatio-temporal dynamics of skeleton data. By learning similarities between different skeleton representations as well as augmented views of the same sequence, the network is encouraged to learn higher-level semantics of the skeleton data than when only using the augmented views. Our approach achieves state-of-the-art performance for self-supervised learning from skeleton data on the challenging PKU and NTU datasets with multiple downstream tasks, including action recognition, action retrieval and semi-supervised learning. Code is available<\/a> as well.<\/p>\n\n\n\n

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The ACM Multimedia 2021 paper Skeleton-Contrastive 3D Action Representation Learning by Fida Thoker, Hazel Doughty and Cees Snoek is now available. This paper strives for self-supervised learning of a feature space suitable for skeleton-based action recognition. Our proposal is built upon learning invariances to input skeleton representations and various skeleton augmentations via a noise contrastive […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3883","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\nMM 2021: Skeleton-Contrastive 3D Action Representation Learning - Cees Snoek<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ceessnoek.info\/index.php\/mm-2021-skeleton-contrastive-3d-action-representation-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"MM 2021: Skeleton-Contrastive 3D Action Representation Learning - Cees Snoek\" \/>\n<meta property=\"og:description\" content=\"The ACM Multimedia 2021 paper Skeleton-Contrastive 3D Action Representation Learning by Fida Thoker, Hazel Doughty and Cees Snoek is now available. 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