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Facial image-based kinship verification is a rapidly growing field in computer vision and biometrics. The key to determining whether a pair of facial images has a kin relation is to train a model that can enlarge the margin between the faces that have no kin relation while reducing the distance between faces that have a kin relation. Most existing approaches primarily exploit duplet (i.e., two input samples without cross pair) or triplet (i.e., single negative pair for each positive pair with low-order cross pair) information, omitting discriminative features from multiple negative pairs. These approaches suffer from weak generalizability, resulting in unsatisfactory performance. Inspired by human visual systems that incorporate both low-order and high-order cross-pair information from local and global perspectives, we propose to leverage high-order cross-pair features and develop a novel end-to-end deep learning model called the adaptively weighted $k$ -tuple metric network (AW $k$ -TMN). Our main contributions are three-fold. First, a novel cross-pair metric learning loss based on $k$ -tuplet loss is introduced. It naturally captures both the low-order and high-order discriminative features from multiple negative pairs. Second, an adaptively weighted scheme is formulated to better highlight hard negative examples among multiple negative pairs, leading to enhanced performance. Third, the model utilizes multiple levels of convolutional features and jointly optimizes feature and metric learning to further exploit the low-order and high-order representational power. Extensive experimental results on three popular kinship verification datasets demonstrate the effectiveness of our proposed AW $k$ -TMN approach compared with several state-of-the-art approaches. The source codes and models are released.1

The performance of person reidentification (ReID) has been significantly improved with the development of convolutional neural networks in the past few years. Most existing ReID methods focus on modifying the backbone network architecture to learn feature representations of persons; however, there are only a few methods that explore the potential performance of the network architecture to relearn features. In this article, we propose a self-regulation feature (SRF) scheme based on the network model, called SRFnet, to address the problem of the limited ability of the model for learning person features. The proposed SRFnet utilizes global branches to supervise local branches and deeply mine potential features of the network from the perspective of optimization to obtain more distinguished feature representations of persons. Different from methods that add an attention mechanism, our SRFnet can self-adjust feature learning by only adding loss functions between the local and global features of the network, and it does not require any additional convolution parameters. Extensive experiments on three public datasets show that the proposed SRFnet method achieves the results of mean average precision (mAP)/Rank-1 as 93.65%/95.28% on Market1501, 89.65%/91.79% on DuckMTMC-reID, 83.79%/82.00% on CUHK03 (labeled), and 81.92%/80.21% on CUHK03 (detected) datasets, respectively. Furthermore, experimental results on cross-domain datasets also demonstrate the effectiveness of the proposed SRFnet.

An ionic cascade insertion/cyclization reaction of thia-/selena-functionalized arylisocyanides has been successfully developed for the efficient and practical synthesis of 2-halobenzothiazole/benzoselenazole derivatives. This synthetic protocol, incorporating a halogen atom when forming the five-membered ring of benzothia/selenazoles, is different from the existing ones, where halogenation of the preformed benzothia/selenazole precursors happens. Additionally, a facile access to 2-aminobenzothiazoles is also achieved by the one-pot cascade reaction of 2-isocyanoaryl thioethers, iodine, and amines.