Cover Caption: The cover image is based on the Research Article Efficient virtual data search for annotationfree vehicle reidentification by Zhijing Wan et al., https://doi.org/10.1002/int.22829.

Vehicle reidentification (re-ID) is the task of retrieving the same vehicle across nonoverlapping cameras, which has made significant progress with the help of abundant manually annotated real images. To avoid the time-consuming and tedious labeling of real images, virtual data sets with large-scale synthetic images have recently been constructed to perform annotation-free model training. However, current methods fail to exploit the potential of virtual data search, that is, searching valuable and representative virtual subdata set for efficient training. This paper presents a novel data sampling strategy from both semantic and feature levels to perform an effective data search. The semantic level determines the sample number of each vehicle identity via the consistency constraint of attribute distribution for source domain and target domain; while the feature level searches valuable and representative samples of each vehicle identity. To our knowledge, we are among the first attempts to search effective virtual data to perform annotation-free vehicle re-ID. Extensive cross-domain experiments from virtual vehicle re-ID data sets to real vehicle re-ID data sets show that our data sampling strategy can significantly reduce the training data volume and even boost the re-ID performance.

Visible-infrared person re-identification (VI-ReID) is undoubtedly a challenging cross-modality person retrieval task with increasing appreciation. Compared to traditional person ReID that focuses on person images in a single RGB mode, VI-ReID suffers from additional cross-modality discrepancy due to the different imaging processes of spectrum cameras. Several effective attempts have been made in recent years to narrow cross-modality gap aiming to improve the re-identification performance, but rarely study the key problem of optimizing the search results combined with relevant feedback. In this paper, we present the idea of cross-modality visible-infrared person re-identification combined with human positive feedback. This method allows the user to quickly optimize the search performance by selecting strong positive samples during the re-identification process. We have validated the effectiveness of our method on a public dataset, SYSU-MM01, and results confirmed that the proposed method achieved superior performance compared to the current state-of-the-art methods.