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• W4312934679 abstract "To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role. However, such cross-view training samples could be unavailable under the ISo-lated Camera Supervised (ISCS) setting, e.g., a surveillance system deployed across distant scenes. To handle this challenging problem, a new pipeline is introduced by synthesizing the cross-camera samples in the feature space for model training. Specifically, the feature encoder and generator are end-to-end optimized under a novel method, Camera-Conditioned Stable Feature Generation (CCSFG). Its joint learning procedure raises concern on the stability of generative model training. Therefore, a new feature generator, σ-Regularized Conditional Variational Autoencoder (σ-Reg. CVAE), is proposed with theoretical and experimental analysis on its robustness. Extensive experiments on two ISCS person Re-ID datasets demonstrate the superiority of our CCSFG to the competitors. <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> https://github.com/ftd-Wuchao/CCSFG" @default.
• W4312934679 created "2023-01-05" @default.
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• W4312934679 date "2022-06-01" @default.
• W4312934679 modified "2023-09-30" @default.
• W4312934679 title "Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification" @default.
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• W4312934679 doi "https://doi.org/10.1109/cvpr52688.2022.01960" @default.
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