{"id":552678,"date":"2018-11-22T14:08:01","date_gmt":"2018-11-22T22:08:01","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=552678"},"modified":"2018-11-22T14:11:04","modified_gmt":"2018-11-22T22:11:04","slug":"reinforced-temporal-attention-and-split-rate-transfer-for-depth-based-person-re-identification","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/reinforced-temporal-attention-and-split-rate-transfer-for-depth-based-person-re-identification\/","title":{"rendered":"Reinforced Temporal Attention and Split-Rate Transfer for Depth-Based Person Re-Identification"},"content":{"rendered":"<p>We address the problem of person re-identification from commodity<br \/>\ndepth sensors. One challenge for depth-based recognition is data<br \/>\nscarcity. Our first contribution addresses this problem by introducing<br \/>\nsplit-rate RGB-to-Depth transfer, which leverages large RGB datasets<br \/>\nmore effectively than popular fine-tuning approaches. Our transfer scheme<br \/>\nis based on the observation that the model parameters at the bottom<br \/>\nlayers of a deep convolutional neural network can be directly shared<br \/>\nbetween RGB and depth data while the remaining layers need to be<br \/>\nfine-tuned rapidly. Our second contribution enhances re-identification<br \/>\nfor video by implementing temporal attention as a Bernoulli-Sigmoid<br \/>\nunit acting upon frame-level features. Since this unit is stochastic, the<br \/>\ntemporal attention parameters are trained using reinforcement learning.<br \/>\nExtensive experiments validate the accuracy of our method in person<br \/>\nre-identification from depth sequences. Finally, in a scenario where subjects<br \/>\nwear unseen clothes, we show large performance gains compared to<br \/>\na state-of-the-art model which relies on RGB data.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We address the problem of person re-identification from commodity depth sensors. One challenge for depth-based recognition is data scarcity. Our first contribution addresses this problem by introducing split-rate RGB-to-Depth transfer, which leverages large RGB datasets more effectively than popular fine-tuning approaches. Our transfer scheme is based on the observation that the model parameters at the [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"European Conference on Computer Vision 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