Unsupervised Re-identification Research Articles

Catalyst for Clustering-Based Unsupervised Object Re-identification: Feature Calibration

Clustering-based methods are emerging as a ubiquitous technology in unsupervised object Re-Identification (ReID), which alternate between pseudo-label generation and representation learning. Recent advances in this field mainly fall into two groups: pseudo-label correction and robust representation learning. Differently, in this work, we improve unsupervised object ReID from feature calibration, a completely different but complementary insight from the current approaches. Specifically, we propose to insert a conceptually simple yet empirically powerful Feature Calibration Module (FCM) before pseudo-label generation. In practice, FCM calibrates the features using a nonparametric graph attention network, enforcing similar instances to move together in the feature space while allowing dissimilar instances to separate. As a result, we can generate more reliable pseudo-labels using the calibrated features and further improve subsequent representation learning. FCM is simple, effective, parameter-free, training-free, plug-and-play, and can be considered as a catalyst, increasing the ’chemical reaction’ between pseudo-label generation and representation learning. Moreover, it maintains the efficiency of testing time with negligible impact on training time. In this paper, we insert FCM into a simple baseline. Experiments across different scenarios and benchmarks show that FCM consistently improves the baseline (e.g., 8.2% mAP gain on MSMT17), and achieves the new state-of-the-art results. Code is available at: https://github.com/lhf12278/FCM-ReID.

Deeply Unsupervised Patch Re-Identification for Pre-Training Object Detectors.

Unsupervised pre-training aims at learning transferable features that are beneficial for downstream tasks. However, most state-of-the-art unsupervised methods concentrate on learning global representations for image-level classification tasks instead of discriminative local region representations, which limits their transferability to region-level downstream tasks, such as object detection. To improve the transferability of pre-trained features to object detection, we present Deeply Unsupervised Patch Re-ID (DUPR), a simple yet effective method for unsupervised visual representation learning. The patch Re-ID task treats individual patch as a pseudo-identity and contrastively learns its correspondence in two views, enabling us to obtain discriminative local features for object detection. Then the proposed patch Re-ID is performed in a deeply unsupervised manner, appealing to object detection, which usually requires multi-level feature maps. Extensive experiments demonstrate that DUPR outperforms state-of-the-art unsupervised pre-trainings and even the ImageNet supervised pre-training on various downstream tasks related to object detection.

Viewpoint Disentangling and Generation for Unsupervised Object Re-ID

Unsupervised object Re-ID aims to learn discriminative identity features from a fully unlabeled dataset to solve the open-class re-identification problem. Satisfying results have been achieved in existing unsupervised Re-ID methods, primarily trained with pseudo-labels created by feature clustering. However, the viewpoint variation of objects is the key challenge, introducing noisy labels in the clustering process. To address this problem, a novel viewpoint disentangling and generation framework (VDG) is proposed to learn viewpoint-invariant ID features, including a disentangling and generation module, as well as a contrastive learning module. First, we design an ID encoder to map the viewpoint and identity features into the latent space. Second, a generator is used to disentangle view features and synthesize images with different orientations. Especially, the well-trained encoder serves as a pre-trained feature extractor in the contrastive learning module. Third, a viewpoint-aware loss and a class-level loss are integrated to facilitate contrastive learning between original and novel views. The generation of novel view images and the application of viewpoint-aware contrastive loss mutually assist model learning viewpoint-invariant ID features. Extensive experiments on Market-1501, DukeMTMC, MSMT17, and VeRi-776 demonstrate the effectiveness of the proposed VDG framework, as well as its superiority over the existing state-of-the-art approaches. The VDG model also demonstrates high quality in the image generation tasks.

Relation-Preserving Feature Embedding for Unsupervised Person Re-Identification

Some unsupervised approaches have been proposed recently for the person re-identification (ReID) problem since annotations of samples across cameras are time-consuming. However, most of these methods focus on the appearance content of the sample itself, and thus seldom take the structure relations among samples into account when learning the feature representation, which would provide a valuable guide for learning the representations of the samples. Thus hard samples may not be well solved due to the limited or even misleading information of the sample itself. To address this issue, in this paper, we propose a Relation-Preserving Feature Embedding (RPE) model that leverages structure relations among samples to boost the performance of the unsupervised person ReID methods without requiring any sample annotations. RPE aims at integrating the sample content and the neighborhood structure relations among samples into the learning of feature embeddings by combining the advantages of the autoencoder and graph autoencoder. Specifically, a relation and content information fusion (RCIF) module is proposed to dynamically merge the information from both perspectives of content and relation levels for feature embedding learning. Also, due to the lack of the identity labels of samples, we adopt an adaptive optimization strategy to update the affinity relations among samples instead of the reconstruction of the whole affinity matrix for optimizing the RPE model, which is more suitable for the unsupervised ReID task. Rigorous experiments on three widely-used large-scale benchmarks for person ReID demonstrate the superiority of the proposed method over current state-of-the-art unsupervised methods.

Weakly Supervised Contrastive Learning for Unsupervised Vehicle Reidentification.

Reidentification (Re-id) of vehicles in a multicamera system is an essential process for traffic control automation. Previously, there have been efforts to reidentify vehicles based on shots of images with identity (id) labels, where the model training relies on the quality and quantity of the labels. However, labeling vehicle ids is a labor-intensive procedure. Instead of relying on expensive labels, we propose to exploit camera and tracklet ids that are automatically obtainable during a Re-id dataset construction. In this article, we present weakly supervised contrastive learning (WSCL) and domain adaptation (DA) techniques using camera and tracklet ids for unsupervised vehicle Re-id. We define each camera id as a subdomain and tracklet id as a label of a vehicle within each subdomain, i.e., weak label in the Re-id scenario. Within each subdomain, contrastive learning using tracklet ids is applied to learn a representation of vehicles. Then, DA is performed to match vehicle ids across the subdomains. We demonstrate the effectiveness of our method for unsupervised vehicle Re-id using various benchmarks. Experimental results show that the proposed method outperforms the recent state-of-the-art unsupervised Re-id methods. The source code is publicly available on https://github.com/andreYoo/WSCL_VeReid.

Unsupervised person Re-identification: A review of recent works

Re-identification (Re-ID) is a process that seeks to identify concern individuals from successive non-overlapping photographs. The area of computer vision has recently seen an uptick in the amount of attention focused on deep neural networks, especially given the popularity of smart monitoring systems and the development of sophisticated learning algorithms. We classified existing Re-ID technologies into closed-world and open-world contexts based on the used components. The closed-world scenario has been commonly used under a variety of data analysis hypotheses, and it brought precise results when applied to a variety of datasets utilizing deep learning techniques. We began with a comprehensive overview of closed-world person Re-ID considering deep metric learning, an extensive representation of features learning, ranking optimization, and in-depth analysis. Due to the accomplishment of performance in the packed scenario, the Re-ID focuses research has lately turned to a bare environment setting, which brings new issues. This setting is more akin to what we'd find in real-world circumstances. We summarized the unsupervised Re-ID literature as well as current research trends and proposed future studies.

HSP-MFL: A High-level Semantic Property driven Multi-task Feature Learning Network for unsupervised person Re-ID

Unsupervised person re-identification aims to distinguish different pedestrians from discriminative representations on the basis of unlabeled data. Currently, most unsupervised Re-ID approaches explore visual representations to generate pseudo-labels for model’s training, which may suffer from background noise and semantic loss. To tackle this problem, this paper proposes a High-level Semantic Property driven Multi-task Feature Learning Network (HSP-MFL) to firstly introduce three high-level semantic properties for unsupervised person Re-ID. Technically, we design a novel Multiple Feature Fusion Module (MFFM) to deeply explore the complex correlation among multiple semantic and visual features to capture the discriminative feature cues, as well as a multi-task training scheme to generate robust fusion features. The architecture is quite simple and does not consume extra labeling costs. Extensive experiments on three datasets demonstrate that both high-level semantic properties and multi-task learning are effective in performance improvement, yielding SOTA mAPs for unsupervised person Re-ID.

Multi-Context Grouped Attention for Unsupervised Person Re-Identification

Recent advancements like multiple contextual analysis, attention mechanisms, distance-aware optimization, and multi-task guidance have been widely used for supervised person re-identification (ReID), but the implementation and effects of such methods in unsupervised ReID frameworks are non-trivial and unclear, respectively. Moreover, with increasing size and complexity of image-and video-based ReID datasets, manual or semi-automated annotation procedures for supervised ReID are becoming labor intensive and cost prohibitive, which is undesirable especially considering the likelihood of annotation errors increase with scale/complexity of data collections. Therefore, we propose a new iterative clustering framework that is insensitive to annotation errors and over-fitting ReID annotations (i.e., labels). Our proposed unsupervised framework incorporates (a) a novel multi-context group attention architecture that learns a holistic attention map from multiple local and global contexts, (b) an unsupervised clustering loss function that down-weights easily discriminative identities, and (c) a background diversity term that helps cluster persons across different cross-camera views without leveraging any identification or camera labels. We perform extensive analysis using the DukeMTMC-VideoReID and MARS video-based ReID datasets and the MSMT17 image-based ReID dataset. Our approach is shown to provide a new state-of-the-art performance for unsupervised ReID, reducing the rank-1 performance gap between supervised and unsupervised ReID to 1.1%, 12.1%, and 21.9% from 6.1%, 17.9%, and 22.6% for DukeMTMC, MARS, and MSMT17 datasets, respectively.

Multi-Level Progressive Learning for Unsupervised Vehicle Re-Identification

Vehicle re-identification (ReID) technology has played a more and more important role in Intelligent Transport System (ITS), which aims at searching the same query vehicle identity from a large amount of gallery datasets under different non-overlapping camera views. Current related researches mainly focus on discriminative feature mining of vehicle images and train the model in a fully supervised manner which highly relies on the manual annotations of training data. However, it is labor-consuming and impractical to generate the annotation for each sample image in real-word applications especially for those large-scale transport systems with tons of surveillance data. To this point, we propose in this paper a multi-level progressive learning (MLPL) method for unsupervised vehicle ReID, which gives a good performance by only utilizing the unlabeled target domain images. We firstly introduce a multi-branch architecture to explore the vehicle representations in different level, which consists of one branch for global feature and two branches for local feature learning. A density-based clustering method is employed to generate pseudo labels. Combining with the unique model, we propose a novel re-clustering method to better mine the labels with high reliability. Then a dynamic progressive contrast learning (DPCL) strategy is carefully designed to train the network based on these clustered labels. DPCL could dynamically adjust the training process to maximally strengthen the multi-level feature learning. Moreover, we further propose a self-adaptive loss balance method to automatically compute the weights of different losses during each training iteration. Comprehensive experiments are conducted on several mainstream evaluation datasets, including VeRi776, VehicleID and CityFlowV2-ReID. Compared to other existed unsupervised methods, our approach achieves the new state-of-the-art performance.

Hybrid partial-constrained learning with orthogonality regularization for unsupervised person re-identification

Person re-identification (re-ID) aims at determining whether there is a specific person in image sets or videos via computer vision technology. State-of-the-art unsupervised re-ID methods extract image features through CNNs-based networks and store these extracted features in memory for identity matching. However, extracted global features of these methods ignore the problem of information redundancy and the influence of the constraints between the internal features. To overcome these problems, a Hybrid Partial-constrained Learning (HPcL) network with orthogonality regularization is proposed to learn a discriminative visual representation by generating hybrid features. Specifically, the hybrid features are generated by our designed Dynamic Fusion Module (DFM) to initialize the memory dictionary and match the identity, which can constrain each part of the features extracted by our proposed Multi-Scale (M-S) module and learn robust visual representations. In addition, a new orthogonal regularization method is introduced to constrain orthogonality of the kernel weights and features, which reduces the correlations among features. Extensive experimental results on Market-1501, DukeMTMC-reID, PersonX, and MSMT17 datasets demonstrate that our method is effective and superior to the state-of-the-art methods.

Diversity feature constraint based on heterogeneous data for unsupervised person re-identification

Person re-identification (ReID) based on heterogeneous data aims to search for the same pedestrian from different modalities. The existing unsupervised heterogeneous ReID method overly relies on pseudo labels and ignores the inter-image feature relationship. In the paper, we propose a novel Diversity Feature Constraint (DFC) method to simultaneously consider the clustering-level and instance-level feature relationship for unsupervised heterogeneous ReID. On the one hand, we employ the clustering algorithm to produce pseudo labels for heterogeneous images. Then, the clustering-level constraint is designed to optimize the model. On the other hand, considering that the clustering algorithm may generate some noise, we propose the complementary intra-modality instance-level constraint to correlate any two intra-modality images. Meanwhile, for eliminating the inter-modality discrepancy, the inter-modality instance-level constraint is developed to decrease the large inter-modality gap. We construct the potential feature relationship between heterogeneous images to constrain the feature distribution. By experiments, we prove that over-reliance on pseudo labels generates limited performance. Exploring inter-image potential relationships is an important way to solve the unsupervised problem. Extensive results demonstrate that DFC achieves superior performance that outperforms other methods by a large margin, improving 15.23% and 9.37% at rank-1 and mAP indexes compared with the clustering method on SYSU-MM01.

Pseudo-Label Noise Prevention, Suppression and Softening for Unsupervised Person Re-Identification

Unsupervised person re-identification (ReID), including fully unsupervised ReID and unsupervised domain adaptive ReID, remains a challenge for the fields of biometrics and computer vision due to its difficulty in learning with unlabeled target domain data. Existing state-of-the-art methods, most of which generate pseudo-labels via unsupervised clustering for model optimization, are inevitably hampered by the under-explored problem of pseudo-label noise. Motivated by this, we propose a novel joint framework termed pseudo-label Noise Prevention, Suppression, and Softening (NPSS) for unsupervised person re-identification. Instead of refining generated label noise after clustering as many existing methods do, we start solving this issue from the source of pseudo-label noise by proposing a new Dynamic Camera-Adaptive Clustering (DCAC), which dynamically involves camera information to prevent noise caused by cross-camera variance, thus improving their quality during clustering. Moreover, we propose an Online Domain Union (ODU) mechanism for the classification model learning on the target domain via involving source domain data with their ground-truth labels, which effectively suppresses the indelible noisy pseudo-labels. Furthermore, we present the Self-Consistency Constraint (SCC) to soften the label noise in a single model with reduced computation and network parameter cost, which achieves intra-sample knowledge ensembling with our global-local SCC and cross-sample knowledge ensembling with our inter-instance SCC. Experiments demonstrate the effectiveness of our method as it surpasses state-of-the-art methods by a large margin on Market-1501, DukeMTMC-ReID, and MSMT17 benchmarks. The code is available at https://github.com/hjwang-824/NPSS.

Cross-domain unsupervised Re-ID algorithm based on neighbor adversarial and consistency loss

Cross-domain unsupervised Re-ID algorithm based on neighbor adversarial and consistency loss

Global Relation-Aware Contrast Learning for Unsupervised Person Re-Identification

The goal of unsupervised person re-identification (Re-ID) is to use unlabeled person images to learn discriminative features. In recent years, many approaches have adopted clustered pseudo labels to construct proxies for contrastive learning, and have thereby achieved great success. However, existing methods of this kind only utilize local structures within IDs to design their proxies while ignoring the relations between samples of different IDs, which limits the improvement for inter-ID discriminative ability. To resolve this issue, we propose a Global Relation-Aware Contrast Learning (GRACL) method for the task of unsupervised Re-ID. Our method first sets up two proxies for each cluster to capture the inter- and intra-ID relations respectively, which enables us to both effectively increase inter-ID variances and reduce the intra-ID discrepancies. Specifically, the samples that are most different from those in different clusters are selected as inter-ID relation-aware proxies, while those that are least similar to samples from the same clusters are employed as intra-ID relation-aware proxies. With the aid of these proxies, we design both inter- and intra-ID relation-aware contrastive learning modules to facilitate model learning. By pulling each sample close to the positive proxy, we can obtain identity-invariant discriminative features. Experiments on five widely-used Re-ID datasets prove that our GRACL model outperforms current state-of-the-art approaches to a remarkable extent.

Viewpoint-Aware Progressive Clustering for Unsupervised Vehicle Re-Identification

Vehicle re-identification (Re-ID) is an active task due to its importance in large-scale intelligent monitoring in smart cities. Despite the rapid progress in recent years, most existing methods handle vehicle Re-ID task in a supervised manner, which is both time and labor-consuming and limits their application to real-life scenarios. Recently, unsupervised person Re-ID methods achieve impressive performance by exploring domain adaption or clustering-based techniques. However, one cannot directly generalize these methods to vehicle Re-ID since vehicle images present huge appearance variations in different viewpoints. To handle this problem, we propose a novel viewpoint-aware clustering algorithm for unsupervised vehicle Re-ID. In particular, we first divide the entire feature space into different subspaces according to the predicted viewpoints and then perform a progressive clustering to mine the accurate relationship among samples. Comprehensive experiments against the state-of-the-art methods on two multi-viewpoint benchmark datasets VeRi-776 and VeRi-Wild validate the promising performance of the proposed method in both with and without domain adaption scenarios while handling unsupervised vehicle Re-ID.

Multi-Centroid Representation Network for Domain Adaptive Person Re-ID

Recently, many approaches tackle the Unsupervised Domain Adaptive person re-identification (UDA re-ID) problem through pseudo-label-based contrastive learning. During training, a uni-centroid representation is obtained by simply averaging all the instance features from a cluster with the same pseudo label. However, a cluster may contain images with different identities (label noises) due to the imperfect clustering results, which makes the uni-centroid representation inappropriate. In this paper, we present a novel Multi-Centroid Memory (MCM) to adaptively capture different identity information within the cluster. MCM can effectively alleviate the issue of label noises by selecting proper positive/negative centroids for the query image. Moreover, we further propose two strategies to improve the contrastive learning process. First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intra-domain information by comparing samples only from the same domain. Second, we propose Second-Order Nearest Interpolation (SONI) to obtain abundant and informative negative samples. We integrate MCM, DSCL, and SONI into a unified framework named Multi-Centroid Representation Network (MCRN). Extensive experiments demonstrate the superiority of MCRN over state-of-the-art approaches on multiple UDA re-ID tasks and fully unsupervised re-ID tasks.

Median Stable Clustering and Global Distance Classification for Cross-Domain Person Re-Identification

The person re-identification (ReID) method in a single-domain achieves appealing performance, but its reliance on label information greatly limits its extensibility. Therefore, the unsupervised cross-domain ReID method has received extensive attention. Its purpose is to optimize the model by using the labelled source domain and the unlabelled target domain and finally make the model well generalized in the target domain. We propose an unsupervised cross-domain ReID method based on median stable clustering (MSC) and global distance classification (GDC). Specifically, the measurement method used by MSC comprehensively considers the similarity between clusters, the number of samples in a cluster, and the combined similarity within a cluster. Different from the method based on triple loss, GDC can separate the distance distribution of positive and negative sample pairs in a global scope. In addition, considering that model performance is very sensitive to probability parameters when source domain memory is reconsolidated, we designed a dynamic memory reconsolidation (DMR) method to reduce the influence of parameters on performance. Extensive experiments on large-scale datasets (Market-1501, DukeMTMC-reID and MSMT17) demonstrate the superior performance of MSC-GDC over the state-of-the-art methods.

Consistent camera-invariant and noise-tolerant learning for unsupervised person re-identification

Unsupervised person re-identification (re-ID) is still a challenging task. Existing methods usually utilize an alternative manner of generating pseudo labels by clustering and optimizing the model based on pseudo labels. Although these methods achieve great accuracy, there remain two problems unsolved: (1) Noise labels caused by camera variations and other factors. (2) The training of the model is inconsistent or unstable. In this paper, we propose a cluster memory-based meta learning (CMML) strategy with a cluster memory-based additive margin (CMAM) loss to deal with noise labels caused by camera variations and the model training problem, and a cluster memory-based noise-tolerant (CMNT) loss to tackle the rest noise labels. Extensive experimental results on three re-ID datasets (i.e., DukeMTMC-reID, Market1501 and MSMT17) validate the effectiveness of our proposed method. Our method achieves 70.3%, 81.8%, and 39.2% mAP on these three datasets, yielding comparable performance against the state-of-the-art purely unsupervised re-ID methods.

Unsupervised person re‐identification by part‐compensated soft multi‐label learning

Most existing re-ID models are developed based on supervised learning, which relies on plenty of manually labelled pairwise data for training. However, in many practical applications, such deep re-ID models are not scalable because of lacking sufficient annotated data. To address this problem, the superiority of unsupervised person re-ID methods have been reported and become more popular recently. In this paper, an unsupervised framework based on part-compensated soft multi-label learning is presented, which aims to explore the potential label information for unlabelled persons through mining both global and part level visual features. This re-ID framework is mainly composed of global clustering, part clustering and multi-label assignment. During clustering process, the results of part-level clustering are compensated into global clustering, allowing the network to learn more detailed information of pedestrians. Meanwhile, fuzzy clustering is used to generate soft multi-labels instead of commonly used single-label clustering. To further optimize the model, a cross-domain excitation parameter is introduced to relieve the background interference arising in the inter-domain view intersection. The proposed approach is evaluated on three public datasets, including Market-1501, DukeMTMC-reID, and MSMT17. Extensive experiments are carried out and this network can outperform the state-of-the-art unsupervised re-ID models.

Adaptive Label Allocation for Unsupervised Person Re-Identification

Most unsupervised methods of person re-identification (Re-ID) obtain pseudo-labels through clustering. However, in the process of clustering, the hard quantization loss caused by clustering errors will make the model produce false pseudo-labels. In order to solve this problem, an unsupervised model based on softened labels training method is proposed. The innovation of this method is that the correlation among image features is used to find the reliable positive samples and train them in a smooth manner. To further explore the correlation among image features, some modules are carefully designed in this article. The dynamic adaptive label allocation (DALA) method which generates pseudo-labels of adaptive size according to different metric relationships among features is proposed. The channel attention and transformer architecture (CATA) auxiliary module is designed, which, associated with convolutional neural network (CNN), functioned as the feature extractor of the model aimed to capture long range dependencies and acquire more distinguishable features. The proposed model is evaluated on the Market-1501 and the DukeMTMC-reID. The experimental results of the proposed method achieve 60.8 mAP on Market-1501 and 49.6 mAP on DukeMTMC-reID respectively, which outperform most state-of-the-art models in fully unsupervised Re-ID task.