{"id":3819,"date":"2021-04-06T08:02:34","date_gmt":"2021-04-06T07:02:34","guid":{"rendered":"https:\/\/www.ceessnoek.info\/?p=3819"},"modified":"2021-04-06T08:02:46","modified_gmt":"2021-04-06T07:02:46","slug":"cvpr-2021-few-shot-transformation-of-common-actions-into-time-and-space","status":"publish","type":"post","link":"https:\/\/www.ceessnoek.info\/index.php\/cvpr-2021-few-shot-transformation-of-common-actions-into-time-and-space\/","title":{"rendered":"CVPR 2021: Few-Shot Transformation of Common Actions into Time and Space"},"content":{"rendered":"\n

The CVPR 2021 cam-ready \u201cFew-Shot Transformation of Common Actions into Time and Space<\/em>\u201d by Pengwan Yang, Pascal Mettes and Cees Snoek is\u00a0now available<\/a>.\u00a0This paper introduces the task of few-shot common action localization in time and space. Given a few trimmed support videos containing the same but unknown action, we strive for spatio-temporal localization of that action in a long untrimmed query video. We do not require any class labels, interval bounds, or bounding boxes. To address this challenging task, we introduce a novel few-shot transformer architecture with a dedicated encoder-decoder structure optimized for joint commonality learning and localization prediction, without the need for proposals. Experiments on reorganizations of the AVA and UCF101-24 datasets show the effectiveness of our approach for few-shot common action localization, even when the support videos are noisy. Although we are not specifically designed for common localization in time only, we also compare favorably against the few-shot and one-shot state-of-the-art in this setting. Lastly, we demonstrate that the few-shot transformer is easily extended to common action localization per pixel.<\/p>\n\n\n\n

\"\"<\/a><\/figure>\n","protected":false},"excerpt":{"rendered":"

The CVPR 2021 cam-ready \u201cFew-Shot Transformation of Common Actions into Time and Space\u201d by Pengwan Yang, Pascal Mettes and Cees Snoek is\u00a0now available.\u00a0This paper introduces the task of few-shot common action localization in time and space. Given a few trimmed support videos containing the same but unknown action, we strive for spatio-temporal localization of that […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-3819","post","type-post","status-publish","format-standard","hentry","category-science"],"yoast_head":"\nCVPR 2021: Few-Shot Transformation of Common Actions into Time and Space - 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\/cvpr-2021-few-shot-transformation-of-common-actions-into-time-and-space\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"CVPR 2021: Few-Shot Transformation of Common Actions into Time and Space - Cees Snoek\" \/>\n<meta property=\"og:description\" content=\"The CVPR 2021 cam-ready \u201cFew-Shot Transformation of Common Actions into Time and Space\u201d by Pengwan Yang, Pascal Mettes and Cees Snoek is\u00a0now available.\u00a0This paper introduces the task of few-shot common action localization in time and space. 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