Visible-Infrared person re-identification technology aims to match the target persons across the visible and infrared modalities. In this paper, we propose a visible-infrared person re-identification method based on a modal-identity dual-central loss. Modal-identity dual-central loss constrains the network to extract modal shared features by pulling in the infrared modal center and visible modal center of the same identity person, while pushing away the identity centers of different person to maintain inter-class discriminability. In addition, to extract more discriminative information, we propose a feature pyramid integration network based on efficient channel attention. Specifically, the network fuses high-level features and fine-grained low-level features to build a multi-scale feature map, and introduces an efficient channel attention module to enhance the salient features of person. Extensive experiments have been conducted to validate our proposed method on the SYSU-MM01 and RegDB datasets.
Visible-infrared person re-identification (VI-ReID) aims to search person images across cameras of different modalities, which can address the limitation of visible-based ReID in dark environments. It is a very challenging task, as images of the same identity have huge discrepancy in different modalities. To address this problem, a cross-modality ReID model based on sample diversity and identity consistency is proposed in this paper. For sample diversity, auxiliary images are introduced based on the idea of information transfer. The auxiliary images combine the information of visible images and infrared images, and can improve the diversity of input data and robustness of the network. For identity consistency, homogeneous distance loss and heterogeneous distance loss are developed from four different perspectives to shorten the distance between the samples of same identities. Extensive experimental results demonstrate the effectiveness of the proposed method.
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