Chenggang Yan

TL;DR: An attribute-person recognition (APR) network is proposed, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes, and demonstrates that by learning a more discriminative representation, APR achieves competitive re-IDs performance compared with the state-of-the-art methods.

TL;DR: A one-stage supervised deep hashing framework (SDHP) is proposed to learn high-quality binary codes, and a deep convolutional neural network is implemented to enforce the learned codes to meet the following criterions.

TL;DR: Zhang et al. as discussed by the authors proposed a supervised multi-view hash model which can enhance the multiview information through neural networks, and the proposed method utilizes an effective view stability evaluation method to actively explore the relationship among views, which will affect the optimization direction of the entire network.

TL;DR: The iterative training mechanism is followed but clustering is discarded, since it incurs loss from hard quantization, yet its only product, image-level similarity, can be easily replaced by pairwise computation and a softened classification task.

TL;DR: This paper proposes a supervised multi-view hash model which can enhance the multi- view information through neural networks, and significantly outperforms the state-of-the-art single-view and multi-View hashing methods.