Inter-task association critic for cross-resolution person re-identification

Person images captured by unconstrained surveillance cameras often have low resolutions (LR). This causes the resolution mismatch problem when matched against the high-resolution (HR) gallery images, negatively affecting the performance of person re-identification (re-id). An effective approach is to leverage image super-resolution (SR) along with person re-id in a joint learning manner. However, this scheme is limited due to dramatically more difficult gradients backpropagation during training. In this paper, we introduce a novel model training regularisation method, called Inter-Task Association Critic (INTACT), to address this fundamental problem. Specifically, INTACT discovers the underlying association knowledge between image SR and person re-id, and leverages it as an extra learning constraint for enhancing the compatibility of SR model with person re-id in HR image space. This is realised by parameterising the association constraint which enables it to be automatically learned from the training data. Extensive experiments validate the superiority of INTACT over the state-of-the-art approaches on the cross-resolution re-id task using five standard person re-id datasets

Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories -- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are avaible at: https://github.com/lightChaserX/Awesome-Hetero-reIDComment: Accepted by IJCAI 2020. Project url: https://github.com/lightChaserX/Awesome-Hetero-reI

DRL-GAN: dual-stream representation learning GAN for low-resolution image classification in UAV applications.

Identifying tiny objects from extremely low resolution (LR) UAV-based remote sensing images is generally considered as a very challenging task, because of very limited information in the object areas. In recent years, there have been very limited attempts to approach this problem. These attempts intend to deal with LR image classification by enhancing either the poor image quality or image representations. In this paper, we argue that the performance improvement in LR image classification is affected by the inconsistency of the information loss and learning priority on Low-Frequency (LF) components and High-Frequency (HF) components. To address this LF-HF inconsistency problem, we propose a Dual-Stream Representation Learning Generative Adversarial Network (DRL-GAN).The core idea is to produce super image representations optimal for LR recognition by simultaneously recovering the missing information in LF and HF components, respectively, under the guidance of high-resolution (HR) images. We evaluate the performance of DRL-GAN on the challenging task of LR image classification. A comparison of the experimental results on the LR benchmark, namely HRSC and CIFAR-10, and our newly collected “WIDER-SHIP” dataset demonstrates the effectiveness of our DRL-GAN, which significantly improves the classification performance, with up to 10% gain on average