{"id":487058,"date":"2018-05-20T02:36:43","date_gmt":"2018-05-20T09:36:43","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=487058"},"modified":"2018-10-16T22:25:10","modified_gmt":"2018-10-17T05:25:10","slug":"mict-mixed-3d-2d-convolutional-tube-human-action-recognition","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mict-mixed-3d-2d-convolutional-tube-human-action-recognition\/","title":{"rendered":"MiCT: Mixed 3D\/2D Convolutional Tube for Human Action Recognition"},"content":{"rendered":"
Human actions in videos are three-dimensional (3D) signals. Recent attempts use 3D convolutional neural networks (CNNs) to explore spatio-temporal information for human action recognition. Though promising, 3D CNNs have not achieved high performanceon on this task with respect to their well-established two-dimensional (2D) counterparts for visual recognition in still images. We argue that the high\u00a0 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13562],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-487058","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"IEEE","msr_edition":"Conference on Computer Vision and Pattern 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