Adaptive Attention-Aware Network for unsupervised person re-identification

Intra-camera supervision (ICS) for person reidentification (Re-ID) assumes that identity labels are independently annotated within each camera view and no inter-camera identity association is labeled. It is a new setting proposed recently to reduce the burden of annotation while expect to maintain desirable Re-ID performance. However, the lack of inter-camera labels makes the ICS Re-ID problem much more challenging than the fully supervised counterpart. By investigating the characteristics of ICS, this paper proposes jointly learned camera-specific non-parametric classifiers, together with a hybrid mining quintuplet loss, to perform intra-camera learning. Then, an inter-camera learning module consisting of a graph-based ID association step and a Re-ID model updating step is conducted. Extensive experiments on three large-scale Re-ID datasets show that our approach outperforms all existing ICS works by a great margin. Our approach performs even comparable to state-of-the-art fully supervised methods in two of the datasets.

Cross-dataset transfer learning is an important problem in person re-identification (Re-ID). Unfortunately, not too many deep transfer Re-ID models exist for realistic settings of practical Re-ID systems. We propose a purely deep transfer Re-ID model consisting of a deep convolutional neural network and an autoencoder. The latent code is divided into metric embedding and nuisance variables. We then utilize an unsupervised training method that does not rely on co-training with non-deep models. Our experiments show improvements over both the baseline and competitors’ transfer learning models.

Person reidentification (Re-ID) matches images of the same person captured in disjoint camera views and at different times. To obtain a reliable similarity measurement between images, manually annotating a large amount of pairwise cross-camera-view person images is deemed necessary. However, this kind of annotation is both costly and impractical for efficiently deploying a Re-ID system to a completely new scenario, a new setting of nonoverlapping camera views between which person images are to be matched. To solve this problem, we consider utilizing other existing person images captured in other scenarios to help the Re-ID system in a target (new) scenario, provided that a few samples are captured under the new scenario. More specifically, we tackle this problem by jointly learning the similarity measurements for Re-ID in different scenarios in an asymmetric way. To model the joint learning, we consider that the Re-ID models share certain component across tasks. A distinct consideration in our multitask modeling is to extract the discriminant shared component that reduces the cross-task data overlap in the shared latent space during the joint learning, so as to enhance the target inter-class separation in the shared latent space. For this purpose, we propose to maximize the cross-task data discrepancy on the shared component during asymmetric multitask learning (MTL), along with maximizing the local inter-class variation and minimizing local intra-class variation on all tasks. We call our proposed method the constrained asymmetric multitask discriminant component analysis (cAMT-DCA). We show that cAMT-DCA can be solved by a simple eigen decomposition with a closed form, getting rid of any iterative learning used in most conventional MTL analyses. The experimental results show that the proposed transfer model gains a clear improvement against the related nontransfer and general multitask person Re-ID models.

Semi-supervised person re-identification (Re-ID) is an important approach for alleviating annotation costs when learning to match person images across camera views. Most existing works assume that training data contains abundant identities crossing camera views. However, this assumption is not true in many real-world applications, especially when images are captured in nonadjacent scenes for Re-ID in wider areas, where the identities rarely cross camera views. In this work, we operate semi-supervised Re-ID under a relaxed assumption of identities rarely crossing camera views, which is still largely ignored in existing methods. Since the identities rarely cross camera views, the underlying sample relations across camera views become much more uncertain, and deteriorate the noise accumulation problem in many advanced Re-ID methods that apply pseudo labeling for associating visually similar samples. To quantify such uncertainty, we parameterize the probabilistic relations between samples in a relation discovery objective for pseudo label training. Then, we introduce reward quantified by identification performance on a few labeled data to guide learning dynamic relations between samples for reducing uncertainty. Our strategy is called the Rewarded Relation Discovery (R 2 D), of which the rewarded learning paradigm is under-explored in existing pseudo labeling methods. To further reduce the uncertainty in sample relations, we perform multiple relation discovery objectives learning to discover probabilistic relations based on different prior knowledge of intra-camera affinity and cross-camera style variation, and fuse the complementary knowledge of different probabilistic relations by similarity distillation. To better evaluate semi-supervised Re-ID on identities rarely crossing camera views, we collect a new real-world dataset called REID-CBD, and perform simulation on benchmark datasets. Experiment results show that our method outperforms a wide range of semi-supervised and unsupervised learning methods.

Two-phase self-supervised pretraining for object re-identification

Person re-identification (ReID) is to identify the same person across non-overlapping camera views. After a decade of development, the methods based on deep networks have achieved high performance on benchmarks and become mainstream. In applications, the features of gallery images extracted by deep learning-based methods are stored to speed up the query process and protect the sensitive information contained in the images. Unfortunately, it is demonstrated that turning the images into features cannot properly protect privacy, as these features could be reversed to the corresponding images, revealing the sensitive information they contain. Therefore, for preventing privacy leakage, recent methods learn their features against some feature reversal methods, and most conventional reversal methods focus on minimizing the difference between a reconstruction and its original image. However, there could be many reasonable reconstruction results from a single feature, and the conventional reversal methods will inevitably generate reconstruction results that lie in a different distribution from one of the original images, which cannot properly assess the private information for learning to protect and thus hamper the privacy-protected feature learning. To mitigate this problem, we enforce the reconstructions to follow the same distribution as the original images by the generative adversarial network (GAN). We operate this GAN-based feature reversal module accompanied by the conventional ReID feature extraction module and form a novel GAN-based feature privacy-protected person ReID model, which is expected to protect feature privacy so as against reversal attack and maintain ReID utility. We demonstrate that optimizing ReID model to accommodate privacy protection faces a double adversarial objective and is thus challenging. As a remedy, we design a novel two-step training and lazy update strategy that alternatively optimizes the feature extraction module and stabilizes the update process of the GAN-based feature reversal module. To evaluate the efficiency of the model in balancing its ReID utility and feature privacy protection, we introduce a novel metric called utility-reversibility ratio (URR). Compared with existing privacy-protected feature extraction models, the proposed method achieves a better balance between privacy protection and person ReID performance. Extensive experiments validate that our model can effectively protect feature privacy at a tiny accuracy cost, and validate the effectiveness of our model with the emerging diffusion model.

Unsupervised person re-identification (ReID) has recently gained significant attention from researchers. ReID matches images of the same person from different camera views in various scenes without any labels. Existing clustering methods primarily rely on a fixed threshold (the maximum distance between sample points and clustering centroids) and overlook the importance of adjusting this threshold during continuous model optimization. This mismatch between clustering thresholds and inter- or intra-class spacing reduces clustering accuracy. To address this issue, this study proposes an Adaptive Clustering and Weighted Regularization Contrastive Learning (ACWRCL) framework for unsupervised person ReID. The ACWRCL framework comprises two main components: (1) the Clustering Threshold Adaptive Adjustment (CTAA) module, and (2) the Weighted Regularization Contrastive Learning (WRCL) module. The CTAA module dynamically adjusts the clustering threshold to align with model optimization, ensuring that the threshold remains within an appropriate range to prevent under- or over-robustness in the clustering model. The WRCL module uses the similarity ratio between the query sample and the clustering centroid relative to the overall similarity of all samples with the same labels as the query sample. This ratio is used as the weight in the loss function to penalize incorrect clustering and improve pseudo-label generation accuracy. Extensive experiments on public ReID datasets—Market-1501, MSMT17, Veri776, CUHK03, and PersonX—demonstrate the effectiveness of the proposed method.

This paper introduces a groundbreaking approach to Person Re-identification (Re-ID), significantly enhancing accuracy and robustness through contrastive learning, especially under challenging real-world conditions like occlusions. The proposed method addresses the limitations of traditional supervised learning, which often demands extensive labeled data, and unsupervised learning, which can be less accurate due to issues such as occlusion. Our solution features a neural network designed to extract both global and local features essential for identifying individuals across different camera views. The network employs global pooling to generate overall identity representation and horizontal pooling to capture detailed local features crucial for distinguishing person identities. Additionally, we present a novel module that integrates a feature extraction encoder with an MLP to refine the comparison of features. To further enhance the model's performance, we utilize a contrastive loss function, an advancement over the standard InfoNCE Loss, which effectively differentiates between positive and negative samples. Experimental results demonstrate a significant improvement in Person Re-ID accuracy and reliability across various environments. By integrating global and local feature representation and leveraging contrastive learning, our model advances the state-of-the-art in Person Re-ID technology. This innovation has broad applications in video surveillance and automated monitoring systems, setting a new standard for accuracy and reliability in the field.

Abstract. Person Re-Identification (Re-ID) is the task of matching the same person in multiple images captured by different cameras. Recently deep learning-based Re-ID algorithms demonstrated an exciting progress for terrestrial-based cameras, still, person Re-ID in aerial images poses multiple challenges including occlusion of human feature parts, image distortion, and dynamic camera location. In this paper, we propose a new Person Aerial Re-ID framework Robust to Occlusion and Thermal imagery (ParrotGAN). Our model is focused on cross-modality person Re-ID in aerial images. Furthermore, we collected a new large-scale synthetic multimodal AerialReID dataset with 30k images and 137 person ID. Our ParrotGAN model leverages two strategies to achieve robust performance in the task of person Re-ID in thermal and visible range. Firstly, we use a latent space of StyleGAN2 model to estimate the distance between two images of a person. Specifically, we project each real image into the latent space with a correspondent latent vector z. We use the distance between latent vectors to provide a Re-ID similarity metric. Secondly, we use a generative-adversarial network to translate a color image to a synthetic thermal image. We use synthetic image for a cross-modality Re-ID. We evaluate our ParrotGAN model and basleines on our AerialReID and PRAI-1581 datasets. The results of the evaluation are encouraging and demonstrate that our ParrotGAN model competes with baselines in visible range aerial person Re-ID and outperforms them in the cross-modality setting. We made our code and dataset publicly available.

Joint graph regularized dictionary learning and sparse ranking for multi-modal multi-shot person re-identification

Person re-identification (ReID) is to identify pedestrians observed from different camera views based on visual appearance. It is a challenging task due to large pose variations, complex background clutters and severe occlusions. Recently, human pose estimation by predicting joint locations was largely improved in accuracy. It is reasonable to use pose estimation results for handling pose variations and background clutters, and such attempts have obtained great improvement in ReID performance. However, we argue that the pose information was not well utilized and hasn't yet been fully exploited for person ReID. In this work, we introduce a novel framework called Attention-Aware Compositional Network (AACN) for person ReID. AACN consists of two main components: Pose-guided Part Attention (PPA) and Attention-aware Feature Composition (AFC). PPA is learned and applied to mask out undesirable background features in pedestrian feature maps. Furthermore, pose-guided visibility scores are estimated for body parts to deal with part occlusion in the proposed AFC module. Extensive experiments with ablation analysis show the effectiveness of our method, and state-of-the-art results are achieved on several public datasets, including Market-1501, CUHK03, CUHK01, SenseReID, CUHK03-NP and DukeMTMC-reID.

Person Re-identification (ReID) has attracted considerable interests in recent years, largely driven by the escalating demand for public safety measures. However, the acquisition and handling of sensitive personal data can trigger significant privacy concerns. Federated learning has been introduced as a potential solution to this problem, with the goal of limiting the exposure of sensitive data across different participating entities (clients). Existing methods often depend on labour-intensive data annotations and face difficulties in maintaining cross-domain uniformity. To tackle these challenges, we propose a Federated Unsupervised Cluster-Contrastive (FedUCC) method based on deep learning for Person ReID that follows a generic-to-specific learning strategy. First, FedUCC procures generic knowledge from a conventional federated learning scheme to aggregate and distribute parameters across local clients. Second, specialized knowledge is explored to facilitate client personalization by disentangling client-specific knowledge from generic knowledge through parameter localization. Third, to further enhance effective fine-grained patterns instead of overfitting on specialized client knowledge, we investigate two key aspects: patch-level feature alignment and camera-invariant learning. Comprehensive experiments on eight public benchmark datasets demonstrate the state-of-the-art performance of our proposed method.

Person re-identification (ReID) is an important and practical task which identifies pedestrians across non-overlapping surveillance cameras based on their visual features. In general, ReID is an extremely challenging task due to complex background clutters, large pose variations and severe occlusions. To improve its performance, a robust and discriminative feature extraction methodology is particularly crucial. Recently, the feature alignment technique driven by human pose estimation, that is, matching two person images with their corresponding parts, increases the effectiveness of ReID to a certain extent. However, we argue that there are still a few problems among these methods such as imprecise handcrafted segmentation of body parts, and some improvements can be further achieved. In this paper, we present a novel framework called Mask-Guided Region Attention Network (MGRAN) for person ReID. MGRAN consists of two major components: Mask-guided Region Attention (MRA) and Multi-feature Alignment (MA). MRA aims to generate spatial attention masks and meanwhile mask out the background clutters and occlusions. Moreover, the generated masks are utilized for region-level feature alignment in the MA module. We then evaluate the proposed method on three public datasets, including Market-1501, DukeMTMC-reID and CUHK03. Extensive experiments with ablation analysis show the effectiveness of this method.

A review on video person re-identification based on deep learning

Person re-identification (Re-ID) aims to retrieve a person of interest across multiple nonoverlapping cameras. In the last few years, the construction of aerial person Re-ID datasets has been appealing because visual surveillance through unmanned aerial vehicle (UAV) platforms has become very valuable in real-world scenarios. However, great differences are exhibited between the pedestrian images captured by ground cameras and those captured by UAVs. Person Re-ID methods based on ground person images have difficulty when performing Re-ID based on aerial person images. In this paper, we first propose a novel meta-transfer learning method for person Re-ID in aerial imagery; this approach trains a generalisable Re-ID model to learn discriminative feature representations for aerial person images. Specifically, a meta-learning strategy is introduced to study a feature extractor, and a transfer learning strategy is introduced to utilise and further improve the acquired meta-knowledge. To overcome the convergence speed and recognition accuracy reductions caused by the presence of difficult categories in the given dataset, we propose a learning strategy based on curriculum sampling that is harmonised with our meta-transfer learning framework. In addition, a new metric formulation of sample similarity is introduced based on Mahalanobis distance to improve the optimisation of the model. Extensive comparative evaluation experiments are conducted on the large-scale aerial Re-ID dataset, and the results obtained show that our method achieves a Rank-1 accuracy of 63.63 $$\%$$ and a mean average precision (mAP) of 38.02 $$\%$$ , demonstrating its potential for completing person Re-ID in aerial images. Ablation studies also validate that each component contributes to improving the performance of the model.