Most existing person re-identification methods are effective in short-term scenarios because of their appearance dependencies. However, these methods may fail in long-term scenarios where people might change their clothes. To this end, we propose an adversarial feature disentanglement network (AFD-Net) which contains intra-class reconstruction and inter-class adversary to disentangle the identity-related and identity-unrelated (clothing) features. For intra-class reconstruction, the person images with the same identity are represented and disentangled into identity and clothing features by two separate encoders, and further reconstructed into original images to reduce intra-class feature variations. For inter-class adversary, the disentangled features across different identities are exchanged and recombined to generate adversarial clothes-changing images for training, which makes the identity and clothing features more independent. Especially, to supervise these new generated clothes-changing images, a re-feeding strategy is designed to re-disentangle and reconstruct these new images for image-level self-supervision in the original image space and feature-level soft-supervision in the disentangled feature space. Moreover, we collect a challenging Market-Clothes dataset and a real-world PKU-Market-Reid dataset for evaluation. The results on one large-scale short-term dataset (Market-1501) and five long-term datasets (three public and two we proposed) confirm the superiority of our method against other state-of-the-art methods.

Single-image super-resolution (SISR) task has witnessed great strides in the past few years with the development of deep learning. However, most existing studies concentrate on exploiting much deeper super-resolution networks, which are not friendly to the constrained computation resources. In this work, a lightweight network using progressive residual learning for SISR (PRLSR) is proposed to address this issue. Specifically, a progressive residual block (PRB) is designed to progressively downsample deep features for reducing the redundancy and obtaining refined features. Simultaneously, a high-frequency preserving module is proposed to lower the detail loss caused by resolution reduction in PRB. Furthermore, a residual learning-based architecture with learnable weights is utilized to extract multilevel features and adaptively adjust the contribution of residual mapping and identity mapping in residual structure to accelerate convergence. Experimental results on four benchmarks show that our PRLSR achieves superior performance over state-of-the-art methods with a significantly decreased computational cost.

Nowadays CNN-based stereo image super-resolution(SR) methods have obtained remarkable performance. However, most of existing methods only superficially portrayed the low layer features without considering the uneven distribution of information, which is insufficient because stereo image warping and sub-pixel upsampling require discriminative features to identify corresponding pixels. To address this problem, in this paper, we propose a novel network named Non-local Nested Residual Attention Network (NNRANet). Specifically, a non-local dilated attention module (NDAM) is developed to exploit the rich hierarchical feature and capture the long-range dependencies between pixels. Moreover, we present a nested residual group (NRG) with dense connections and multiple nested residual sub-network, which not only continuously remembers and extracts the stereo fusion feature, but also enables training a deeper and more stable network. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on the Middlebury, KITTI 2012 and KITTI 2015 datasets.