Joint Learning of Single-image and Cross-image Representations for Person Re-identification

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Authors F. Wang, W. Zuo, L. Lin, D. Zhang and L. Zhang
Journal/Conference Name 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016)
Paper Category
Paper Abstract Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR). In this work, we exploit the connection between these two categories of methods, and propose a joint learning frame-work to unify SIR and CIR using convolutional neural network (CNN). Specifically, our deep architecture contain-s one shared sub-network together with two sub-networks that extract the SIRs of given images and the CIRs of given image pairs, respectively. The SIR sub-network is required to be computed once for each image (in both the probe and gallery sets), and the depth of the CIR sub-network is required to be minimal to reduce computational burden. Therefore, the two types of representation can be jointly optimized for pursuing better matching accuracy with moderate computational cost. Furthermore, the representations learned with pairwise comparison and triplet comparison objectives can be combined to improve matching performance. Experiments on the CUHK03, CUHK01 and VIPeR datasets show that the proposed method can achieve favor-able accuracy while compared with state-of-the-arts.
Date of publication 2016
Code Programming Language Caffe
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