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Most previous visible–infrared person re-identification methods emphasized learning modality-shared features to narrow the modality differences, while neglecting the benefits of modality-specific features for feature embedding and narrowing the modality gap. To tackle this issue, our paper designs a method based on dual attention enhancement features to use shallow and deep features simultaneously. We first convert visible images into gray images to alleviate the visual difference. Then, to close the difference between modalities by learning the modality-specific features, we design a shallow feature measurement module, in which we use a class-specific maximum mean discrepancy loss to measure the distribution difference of specific features between two modalities. Finally, we design a dual attention feature enhancement module, which aims to mine more useful context information from modality-shared features to shorter the distance between classes within modalities. Specifically, our model exceeds the current SOTAs on SYSU-MM01, with 66.61% Rank-1 accuracy and 62.86% mAP. [ABSTRACT FROM AUTHOR]

Most recent occluded person re-identification (re-ID) methods usually learn global features directly from pedestrian images, or use additional pose estimation and semantic analysis model to learn local features, while ignoring the relationship between global and local features, thus incorrectly retrieving different pedestrians with similar attributes as the same pedestrian. Moreover, learning local features using auxiliary models brings additional computational cost. In this work, we propose a Transformer-based dual-branch feature learning model for occluded person re-ID. Firstly, we propose a global–local feature interaction module to learn the relationship between global and local features, thus enhancing the richness of information in pedestrian features. Secondly, we randomly erase local areas in the input image to simulate the real occlusion situation, thereby improving the model's adaptability to the occlusion scene. Finally, a spilt group module is introduced to explore the local distinguishing features of pedestrian. Numerous experiments validate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

The image descriptors based on multi-features fusion have better performance than that based on simple feature in content-based image retrieval (CBIR). However, these methods still have some limitations: (1) the methods that define directly texture in color space put more emphasis on color than texture feature; (2) traditional descriptors based on histogram statistics disregard the spatial correlation between structure elements; (3) the descriptors based on structure element correlation (SEC) disregard the occurring probability of structure elements. To solve these problems, we propose a novel image descriptor, called Global Correlation Descriptor (GCD), to extract color and texture feature respectively so that these features have the same effect in CBIR. In addition, we propose Global Correlation Vector (GCV) and Directional Global Correlation Vector (DGCV) which can integrate the advantages of histogram statistics and SEC to characterize color and texture features respectively. Experimental results demonstrate that GCD is more robust and discriminative than other image descriptors in CBIR. [ABSTRACT FROM AUTHOR]