Abstract：In the existing cross-modal person re-identification methods, modal differences are lessened by aligning features or pixel distributions of different modalities. However, the discriminative fine-grained information of pedestrians is ignored in these methods. To obtain more discriminative pedestrian features independent of modal differences, a modal invariance feature learning and consistent fine-grained information mining based cross-modal person re-identification method is proposed. The proposed method is mainly composed of two modules, modal invariance feature learning and semantically consistent fine-grained information mining. The two modules are combined to drive the feature extraction network to obtain discriminative features. Specifically, the modal invariant feature learning module is utilized to remove the modal information from the feature map to reduce the modal differences. Channel grouping and horizontal segmentation are conducted on person feature maps via the semantic consistent fine-grained information mining module. Consequently, the semantic alignment is achieved and the discriminative fine-grained information is fully mined. Experimental results show that the performance of the proposed method is significantly improved compared with the state-of-the-art cross-modal person re-identification methods.
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2022, Vol. 35 
