Traditional person re-identification (re-id) methods perform poorly under changing illuminations. This situation can be addressed by using dual-cameras that capture visible images in a bright environment and infrared images in a dark environment. Yet, this scheme needs to solve the visible-infrared matching issue, which is largely under-studied. Matching pedestrians across heterogeneous modalities is extremely challenging because of different visual characteristics. In this paper, we propose a novel framework that employ modality-specific networks to tackle with the heterogeneous matching problem. The proposed framework utilizes the modality-related information and extracts modality-specific representations (MSR) by constructing an individual network for each modality. In addition, a cross-modality Euclidean constraint is introduced to narrow the gap between different networks. We also integrate the modality-shared layers into modality-specific networks to extract shareable information and use a modality-shared identity loss to facilitate the extraction of modality-invariant features. Then a modality-specific discriminant metric is learned for each domain to strengthen the discriminative power of MSR. Eventually, we use a view classifier to learn view information. The experiments demonstrate that the MSR effectively improves the performance of deep networks on VI-REID and remarkably outperforms the state-of-the-art methods.

Learning discriminative and rich features is an important research task for person re-identification. Previous studies have attempted to capture global and local features at the same time and layer of the model in a non-interactive manner, which are called synchronous learning. However, synchronous learning leads to high similarity, and further defects in model performance. To this end, we propose asynchronous learning based on the human visual perception mechanism. Asynchronous learning emphasizes the time asynchrony and space asynchrony of feature learning and achieves mutual promotion and cyclical interaction for feature learning. Furthermore, we design a dynamic progressive refinement module to improve local features with the guidance of global features. The dynamic property allows this module to adaptively adjust the network parameters according to the input image, in both the training and testing stage. The progressive property narrows the semantic gap between the global and local features, which is due to the guidance of global features. Finally, we have conducted several experiments on four datasets, including Market1501, CUHK03, DukeMTMC-ReID, and MSMT17. The experimental results show that asynchronous learning can effectively improve feature discrimination and achieve strong performance.

Minimizing the computation complexity is essential for the popularization of deep networks in practical applications. Nowadays, most researches attempt to accelerate deep networks by designing new network structure or compressing the network parameters. Meanwhile, transfer learning techniques such as knowledge distillation are utilized to keep the performance of deep models. In this paper, we focus on accelerating deep models and relieving the computation burden by using low-resolution (LR) images as inputs while maintaining competitive performance, which is rarely researched in the current literature. Deep networks may encounter serious performance degradation when using LR inputs because many details are unavailable from LR images. Besides, the existing approaches may fail to learn discriminative features for LR images because of the dramatic appearance variations between LR and high-resolution (HR) images. To tackle with the above problems, we propose a resolution-aware knowledge distillation (RKD) framework to narrow the cross-resolution variations by transferring knowledge from HR domain to LR domain. The proposed framework consists of a HR teacher network and a LR student network. First, we introduce a discriminator and propose an adversarial learning strategy to shrink the variations between inputs with changing resolution. Then we design a cross-resolution knowledge distillation (CRKD) loss to train discriminative student network by exploiting the knowledge of the teacher network. The CRKD loss is consisted of a resolution-aware distillation loss, a pair-wise constraint, and a maximum mean discrepancy loss. Experimental results on person re-identification, image classification, face recognition, and defect segmentation tasks demonstrate that RKD outperforms traditional knowledge distillation method by achieving better performance with lower computation complexities. Furthermore, CRKD surpasses the state-of-the-art knowledge distillation methods in transferring knowledge across different resolutions under RKD framework, especially when coping with large resolution differences.

Face verification technology has been widely used in realist applications such as surveillance, access control and passport authentication. It remains one of the most active research topics in computer vision and pattern recognition. Recently, more research efforts for face verification have focused on uncontrolled environment while current face verification techniques have been proven to be robust and efficient for controlled environment. In this paper, we focus to study on the issue of blur and low resolution (LR), which is common in video surveillance and real application. We propose a descriptor which uses the Fisher Kernel framework to encode the multi-scale absolute phase difference feature of the local image. Then we combine the feature with multiple metric learning approach to achieve a blur robust descriptor that is compact and discriminant. Experiment on blurred ferret dataset and realistic face dataset validates the efficiency of the proposed approach.