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METHOD AND DEVICE FOR QUASI-GIBBS STRUCTURE SAMPLING BY DEEP PERMUTATION FOR PERSON IDENTITY INFERENCE
METHOD AND DEVICE FOR QUASI-GIBBS STRUCTURE SAMPLING BY DEEP PERMUTATION FOR PERSON IDENTITY INFERENCE
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机译:通过深度置换对人的身份推断进行准Gibbs结构采样的方法和装置
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摘要
The present disclosure provides a method and device for visual appearance based person identity inference. The method may include obtaining a plurality of input images. The input images include a gallery set of images containing, persons-of-interest and a probe set of images containing person detections, and one input image corresponds to one person. The method may further include extracting N feature maps from the input images using a Deep Neural Network, N being a natural number; constructing N structure samples of the N feature maps using conditional random field (CRF) graphical models; learning the N structure samples from an implicit common latent feature space embedded in the N structure samples; and according to the learned structures, identifying one or more images from the probe set containing a same person-of-interest as an image in the gallery set.
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