Unsupervised Person Re-identification Research Articles

MCFR: multi-confidence contrastive learning with feature refined for unsupervised person re-identification

MCFR: multi-confidence contrastive learning with feature refined for unsupervised person re-identification

Learning Invariance from Generated Variance for Unsupervised Person Re-identification.

This work focuses on unsupervised representation learning in person re-identification (ReID). Recent self-supervised contrastive learning methods learn invariance by maximizing the representation similarity between two augmented views of a same image. However, traditional data augmentation may bring to the fore undesirable distortions on identity features, which is not always favorable in id-sensitive ReID tasks. In this paper, we propose to replace traditional data augmentation with a generative adversarial network (GAN) that is targeted to generate augmented views for contrastive learning. A 3D mesh guided person image generator is proposed to disentangle a person image into id-related and id-unrelated features. Deviating from previous GAN-based ReID methods that only work in id-unrelated space (pose and camera style), we conduct GAN-based augmentation on both id-unrelated and id-related features. We further propose specific contrastive losses to help our network learn invariance from id-unrelated and id-related augmentations. By jointly training the generative and the contrastive modules, our method achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.

Research on an unsupervised person re-identification based on image quality enhancement method

Research on person re-identification(Re-ID) has important value in pedestrian detection, target tracking, criminal investigation, and other related fields. In unsupervised pedestrian recognition algorithms, the accuracy of pseudo-labels is crucial to the recognition results. However, in practical scenarios, low-quality images caused by factors such as differences in camera resolution and shooting angles can affect the extraction of pedestrian features by these algorithms, thereby negatively impacting the accuracy of the labels and the learning process of the model. To address this problem, we propose an image quality enhancement algorithm for unsupervised person Re-ID (IQE). To the best of our knowledge, this study is the first to introduce detail enhancement and the application of low-light enhancement algorithms into unsupervised person Re-ID. By improving the feature extraction quality based on these two aspects, higher-quality pseudo-labels can be constructed. This method improves the accuracy of feature extraction and clustering, thereby increasing the accuracy of pseudo-labels and reducing the interference of noisy pseudo-labels. The experimental results showed that the IQE method outperformed state-of-the-art person Re-ID methods in terms of Rank-1 accuracy and mAP. Specifically, IQE achieved an 87.9% rank-1 accuracy and a 71.2% mAP on the Market-1501 dataset; a 78.1% rank-1 accuracy and a 61.7% mAP On the DukeMTMC-reID dataset; and a 51.1% rank-1 accuracy and 24.2% mAP on the MSMT17 dataset.

Camera-irrelevant Feature Invariance Learning for Unsupervised Person Re-identification

Unsupervised person re-identification (re-ID) aims to match pictures of target pedestrians from some other cameras without any labels. The existing methods usually adopt a cluster-based pipeline to generate false labels for training. However, because of the differences in camera configuration, taking pictures Angle, illumination intensity and pedestrian background, unlabeled pedestrian training samples captured by different cameras are hard to classify into a cluster. To solve this problem, the paper proposes a camera-irrelevant feature learning framework, which learns camera-irrelevant features and camera-irrelevant distances through network training and clustering. Specifically, during training, images of other camera styles are generated, which enriches cross-camera learning. During clustering, we propose a camera-irrelevant distance matrix, which reduces the inter-class variance caused by camera variance.

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.

Improving pseudo-labeling with reliable inter-camera distance encouragement for unsupervised person re-identification

Improving pseudo-labeling with reliable inter-camera distance encouragement for unsupervised person re-identification

Complementary pseudolabel based on global-and-channel information for unsupervised person reidentification

Unsupervised person reidentification (re-ID) is designed to deal with the problem that in industrial application scenarios, the consistent features of the same person cannot be fully mined due to the lack of annotated information in the collected image resources. Researchers focus on using pseudolabels to label images with the same clustering attributes, but traditional clustering methods are prone to generating noisy pseudolabels, which greatly reduce the accuracy of unsupervised person re-ID. We propose a method to refine pseudolabels by generating discriminative information based on channel partitioning. In the design process, the feature map is divided from the channel level, generating a proximity matrix by calculating the distance between the channel feature and the global feature, and select the most negative sample as the verification label to optimize the pseudolabel of the global feature. As a bidirectional guide, the global pseudolabel can be used to further smooth each channel label, keeping the consistency of channel features and global features in pseudolabel selection. This unique method of learning local features can link global information and channel information. Experimental results demonstrate the superior performance of our proposal on representative person re-ID datasets.

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.

Unsupervised Person Re-identification Using Unified Domanial Learning

Unsupervised Person Re-identification Using Unified Domanial Learning

Discriminatively Unsupervised Learning Person Re-Identification via Considering Complicated Images.

State-of-the-art purely unsupervised learning person re-ID methods first cluster all the images into multiple clusters and assign each clustered image a pseudo label based on the cluster result. Then, they construct a memory dictionary that stores all the clustered images, and subsequently train the feature extraction network based on this dictionary. All these methods directly discard the unclustered outliers in the clustering process and train the network only based on the clustered images. The unclustered outliers are complicated images containing different clothes and poses, with low resolution, severe occlusion, and so on, which are common in real-world applications. Therefore, models trained only on clustered images will be less robust and unable to handle complicated images. We construct a memory dictionary that considers complicated images consisting of both clustered and unclustered images, and design a corresponding contrastive loss by considering both kinds of images. The experimental results show that our memory dictionary that considers complicated images and contrastive loss can improve the person re-ID performance, which demonstrates the effectiveness of considering unclustered complicated images in unsupervised person re-ID.

Pseudo Label Rectification With Joint Camera Shift Adaptation and Outlier Progressive Recycling for Unsupervised Person Re-Identification

Person re-identification (re-ID) has many applications in intelligent transportation systems. Clustering-based methods, which alternate between the generation of pseudo labels via clustering and the optimization of the feature extractor, have obtained leading performance in unsupervised person re-ID. But there are still two issues not well addressed: 1) Most methods measure the feature similarity without considering the domain shift between cameras, degrading the clustering performance. 2) Outliers, which usually correspond to hard samples with large discrepancy from other images of the identical person, are in most cases directly excluded from the network training. To tackle the above issues, this paper proposes a plug-and-play pseudo label rectification framework, which jointly utilizes CAmera Shift adapTation module and Outlier progressive Recycling strategy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CASTOR$ </tex-math></inline-formula> ) to improve the quality of pseudo labels from both pre-clustering and post-clustering. Specifically, we first compute the camera similarity of two samples by utilizing a pretrained camera classification network and subtract the feature similarity by the camera similarity, the value of which is weighted in an exponential decay manner throughout the network training, in order to adaptively remedy the adverse impact of inter-camera distribution shift upon clustering. Besides, we carefully design an outlier progressive recycling strategy to reassign part of the outliers into the clustered groups to make full use of the useful information of outliers. Extensive experiments on three large scale unsupervised and unsupervised domain adaptive (UDA) person re-ID benchmarks validate the effectiveness of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CASTOR$ </tex-math></inline-formula> and its wide compatibility with the state-of-the-art clustering-based methods.

Generative Segment-pose Representation based Augmentation (GSRA) for unsupervised person re-identification

Generative Segment-pose Representation based Augmentation (GSRA) for unsupervised person re-identification

Multiple camera styles learning for unsupervised person re-identification

Most clustering-based unsupervised person re-identification methods usually first assign pseudo-labels to pedestrian images using a clustering algorithm and then train a network using the labeled pedestrian images in each iteration. However, clustering algorithms may produce some clusters that only contain pedestrian images from fewer cameras, thus resulting in these clusters having fewer camera styles. Training a network using the pedestrian images from these clusters may degrade the capability of the network to retrieve pedestrians across cameras. Moreover, unsupervised learning methods usually utilize only contrastive loss that is based on self-supervised learning. However, lack of metric loss with mandatory guidance from labels may lead the network to fail to learn sufficiently the discriminative information of pedestrians. To address these issues, we propose a multiple camera styles learning (MCSL) method for unsupervised person re-identification. Specifically, we design a novel cluster filtration module (CFM) to ensure the clustering algorithm to keep the focus on pedestrian identities rather than camera styles by filtering out the clusters with fewer camera styles. In addition, we also design a multi-granularity feature metric (MGFM) loss to introduce metric learning with labels into unsupervised learning. The effectiveness of the proposed MCSL is demonstrated by the experimental results (Rank-1 accuracy) on Market1501 (93.7%), DukeMTMC(86.0%) and MSMT17 (71.12%), significantly reducing the gap between unsupervised and supervised performance on person re-identification.

Camera-aware progressive learning for unsupervised person re-identification

Camera-aware progressive learning for unsupervised person re-identification

Unsupervised person re-identification via multi-domain joint learning

Deep learning techniques have achieved impressive progress in the task of person re-identification. However, how to generalize a learned model from the source domain to the target domain remains a long-standing challenge. Inspired by the fact that the enrichment of data diversity and the utilization of miscellaneous semantic features can lead to better generalization ability, we design a model that integrates a novel data augmentation method with a multi-label assignment strategy to achieve semantic features decoupling in the source domain. The pre-trained model is employed to extract several kinds of semantic features from the target dataset, and each kind of semantic features is regarded as a specific domain. We then cluster features of each domain and exploit the connection between different clustering results to perform self-distillation for generating more reliable pseudo labels. Finally, the obtained pseudo labels are used to fine-tune the pre-trained model to achieve model transfer from the source domain to the target one. Extensive experiments demonstrate that our approach outperforms some state-of-the-art methods by a clear margin and even surpass some supervised methods. Source code is available at: https://www.github.com/flychen321/MDJL.

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.

Reliability modeling and contrastive learning for unsupervised person re-identification

Unsupervised person re-identification (ReID) aims to learn discriminative identity features in scenarios without a ground-truth. Fully unsupervised person ReID methods usually iterate between pseudo-labels prediction and representation learning and have achieved promising performance. However, these methods are usually hampered by noisy labels. To address this issue, we propose a reliability modeling and contrastive learning (RMCL) method, which aims to reduce the impact of noisy labels and enhance the robustness of the model to hard samples. On the basis of existing work, first, we define the concept of probabilistic stability and design a stability estimation scheme to improve pseudo-labels reliability modeling. Second, we explore the reliability–informativity function to redefine the weights of samples, which can be easily introduced to existing optimization methods. Finally, we expand the range of hard samples and design an identity hard contrastive loss, which is used to increase the robustness of the model to hard samples. Experiments on three large-scale person ReID datasets (Market-1501, DukeMTMC-reID and MSMT17) validate the effectiveness of RMCL, which surpasses state-of-the-art fully unsupervised methods and cross-domain methods. Furthermore, we explore the superiority of RMCL in a new “three2one” scenario.

Recent progress in person re-ID

行人再识别通过大时空范围内跨摄像机目标行人图像的检索与匹配，可实现人脸等生物特征失效情况下的行人关联，已成为智能视频监控系统的关键环节和支撑技术，并在智慧公安、智慧城市等国民经济建设中发挥了重要作用。近年行人再识别技术吸引了越来越多的关注，并取得了快速发展与进步。本文在对行人再识别技术进行简介的基础上，面向行人再识别的技术发展和落地应用需求与挑战，总结分析遮挡行人再识别、无监督行人再识别、虚拟数据生成、域泛化行人再识别、换装行人再识别、跨模态行人再识别和行人搜索等热点方向的前沿进展，归纳其发展现状和存在问题，最后对行人再识别技术的发展趋势进行展望。希望通过总结和分析，能够为研究人员开展行人再识别相关研究、推动行人再识别技术进步提供参考。;Person re-Identification(person re-ID) technique aims to solve the problem of association and matching of target person images across multiple cameras within a camera network of a surveillance system, especially in the case of face, iris and other biometrics recognition failure under non-cooperative application scenarios, and has become one of the key component and supporting technique for intelligent video surveillance systems and applications in intelligent public security and smart cities. Recently, person re-ID has attracted more and more attention from both academia and industry, and has made rapid development and progress. To meet the requirement of its technical challenges and application needs of person re-ID in practical scenarios, this paper will first give a brief introduction to the development history, commonly used datasets and evaluation metrics. Then, the recent progress in hot research topics of person re-ID is extensively reviewed and analyzed, which includes:occluded person re-ID, unsupervised person re-ID, virtual data generation, domain generalization, clothchanging person re-ID, cross-modal person re-ID and person search. First, to address the problem of impact of possible occlusions on the performance of person re-ID, recent progress in occluded person re-ID is first reviewed, in which the popular datasets for occluded person re-ID are briefly introduced, and the two major categories of occluded person re-ID models are then further reviewed. Second, facing the challenges of low-efficiency and high-cost data annotation and great impact of training data on the performance of person re-ID, unsupervised person re-ID and virtual data generation for person re-ID emerges as two hot topics in person re-ID. The paper elaborates the recent advances of unsupervised person reID, which can be classified into three major categories:pseudo label generation-based models, domain transfer-based models, and other related models, which take into consideration of the extra information like time-stamps, camera labels besides person image. Third, the-state-of-the-art works on virtual data generation for person re-ID are reviewed, with detailed introduction and performance comparisons of major virtual datasets. Fourth, recent researches on domain generalization person re-ID will be reviewed, which are classified into five categories:batch/instance normalization models, domain invariant feature learning models, deep-learning-based explicit image matching models, models based on mixture of experts and meta-learning-based models. Fifth, since most current person re-ID models largely depend on the color appearance of persons' clothes, clothes-changing person re-ID becomes a challenging setting, in which person images can exhibit large intra-class variation and small inter-class variation. Typical cloth-changing person re-ID datasets are introduced and the recent researches will then be reviewed, in which models in the first category explicitly introduces extra cloth-appearance-independent features like contour and face while the second try to decouple the cloth features and person ID features. Sixth, to compensate the drawbacks of conventional person re-ID of visible light/RGB images in natural complex scenes like poor lighting conditions in the night, the-state-of-the-art of cross-modal person re-ID, which aims to resolve the problem through other visible RGB images-excluded heterogeneous data, are reviewed, with a brief introduction of commonly used cross-modal person re-ID datasets first and then four sub-categories models according to the different modalities employed, including:RGB-infrared image person re-ID, RGB image-text person re-ID, RGB image-sketch person re-ID, and RGB-depth image person re-ID, respectively. Seventh, since existing person re-ID benchmarks and methods mainly focus on matching cropped person images between queries and candidates and is different from practical scenarios where the bounding box annotations of persons are often unavailable, person search, which jointly considers person detection and person re-ID in a single framework, becomes a new hot research topic. The typical datasets and recent progress on person search are reviewed. Finally, the existing challenges and development trend of person re-ID techniques are discussed. It is hoped that the summary and analysis can provide reference for relevant researchers to carry out research on person re-ID and promote the progress of person re-ID techniques and applications.

Learning to Purification for Unsupervised Person Re-Identification.

Unsupervised person re-identification is a challenging and promising task in computer vision. Nowadays unsupervised person re-identification methods have achieved great progress by training with pseudo labels. However, how to purify feature and label noise is less explicitly studied in the unsupervised manner. To purify the feature, we take into account two types of additional features from different local views to enrich the feature representation. The proposed multi-view features are carefully integrated into our cluster contrast learning to leverage more discriminative cues that the global feature easily ignored and biased. To purify the label noise, we propose to take advantage of the knowledge of teacher model in an offline scheme. Specifically, we first train a teacher model from noisy pseudo labels, and then use the teacher model to guide the learning of our student model. In our setting, the student model could converge fast with the supervision of the teacher model thus reduce the interference of noisy labels as the teacher model greatly suffered. After carefully handling the noise and bias in the feature learning, our purification modules are proven to be very effective for unsupervised person re-identification. Extensive experiments on two popular person re-identification datasets demonstrate the superiority of our method. Especially, our approach achieves a state-of-the-art accuracy 85.8% @mAP and 94.5% @Rank-1 on the challenging Market-1501 benchmark with ResNet-50 under the fully unsupervised setting. Code has been available at: https://github.com/tengxiao14/Purification_ReID.

Hybrid Contrastive Learning for Unsupervised Person Re-Identification

Unsupervised person re-identification (Re-ID) aims to learn discriminative features without human-annotated labels. Recently, contrastive learning provides a new prospect for unsupervised person Re-ID, and existing methods mainly constrain the feature similarity among easy sample pairs. However, the feature similarity among hard sample pairs is neglected, which causes suboptimal performance in unsupervised person Re-ID. In the paper, we propose a novel Hybrid Contrastive Model (HCM) to perform the identity-level contrastive learning and the image-level contrastive learning for unsupervised person Re-ID, which adequately explores the feature similarity among hard sample pairs. Specifically, for the identity-level contrastive learning, an identity-based memory is constructed to store pedestrian features. Accordingly, we define the dynamic contrast loss to identify identity information with dynamic factor for distinguishing hard/easy samples. As for the image-level contrastive learning, an image-based memory is established to store each image feature. We design the sample constraint loss to explore the similarity relationship between hard positive and negative sample pairs. Furthermore, we optimize the two contrastive learning processes in one unified framework to make use of their own advantages as so to constrain the feature distribution for extracting potential information. Extensive experiments prove that the proposed HCM distinctly outperforms the state-of-the-art methods.