Deep Representation Learning With Part Loss for Person Re-Identification

Learning discriminative representations for unseen person images is critical for person Re-Identification (ReID). Most of current approaches learn deep representations in classification tasks, which essentially minimizes the empirical classification risk on the training set. As shown in our experiments, such representations easily get over-fitted on a discriminative human body part on the training set. To gain the discriminative power on unseen person images, we propose a deep representation learning procedure named Part Loss Network (PL-Net), to minimize both the empirical classification risk on training person images and the representation learning risk on unseen person images. The representation learning…

• NN
  National Natural Science Foundation of China

  Awards: U1705262, 91538111, 61620106009, 61721004, 61532009, 61572050, 61525206