Cross-domain Person Re-identification Research Articles

Cross-Domain Person Re-Identification Based on Feature Fusion Invariance

Cross-domain person re-identification is a technique for identifying the same individual across different cameras or environments that necessitates the overcoming of challenges posed by scene variations, which is a primary challenge in person re-identification and a bottleneck for its practical applications. In this paper, we learn the invariance model of cross-domain feature fusion in a labeled source domain and an unlabeled target domain. First, our method learns the global and local fusion features of a person in the source domain by means of supervised learning with no component label and only person identification and obtains the fusion features of the person in the source and target domains by means of unsupervised learning. Based on person fusion features, this paper introduces feature memory to store the fused target features and designs a cross-domain invariance loss function to improve the cross-domain adaptability of the person. Finally, this paper carries out cross-domain person re-identification verification experiments between the Market-1501 and DukeMTMC-reID datasets; the experimental results show that the proposed method achieves significant performance improvement in cross-domain person re-identification.

Cross-Domain Person Re-Identification Based on Normalized IBN-Net

Cross-Domain Person Re-Identification Based on Normalized IBN-Net

Cross-domain person re-identification with normalized and enhanced feature

Cross-domain person re-identification with normalized and enhanced feature

The triple refinement of self-paced learning style for unsupervised cross-domain person re-identification

Unsupervised cross-domain person re-identification based on clustering typically involves generating pseudo-labels for all training data. However, some data are prone to generating incorrect pseudo-labels in the early stages of training, which leads to a decrease in the accuracy of the model. Based on the idea of self-paced learning, from easy to difficult and from a few to many samples, we propose the Triple Refinement of Self-paced Learning style (TRSL) method to solve this problem. Firstly, we use the density-based clustering algorithm DBSCAN to perform the first data refinement on the unlabeled data. Then, in the second refinement step, we calculate the challenge value by exploring the distance distribution between the clustering data and the centroids to refine the data. In the third refinement step, we calculate the credibility value of the data by comparing the k-nearest neighbor ranking lists of the local and global features of the images, use the credibility value to recall the discarded data from the dual refinement process, and perform a third filtering on the retained data. Finally, the processed data is input into a pair of mutually beneficial learning models for training. On top of a strong baseline, our method performs better than the recent state-of-the-art methods in some evaluation metrics.

Weakly Supervised Cross-Domain Person Re-Identification Algorithm Based on Small Sample Learning

This paper proposes a weakly supervised cross-domain person re-identification (Re-ID) method based on small sample data. In order to reduce the cost of data collection and annotation, the model design focuses on extracting and abstracting the information contained in the data under limited conditions. In this paper, we focus on the problems of strong data dependence, weak cross-domain capability and low accuracy in Re-ID in weakly supervised scenarios. Our contributions are as follows: first, we implement a joint training framework with the help of small sample learning and cross-domain migration for Re-ID. Second, with the help of residual compensation and fusion attention module, the RCFA module is designed, and the model framework is built on this basis to improve the cross-domain ability of the model. Third, to solve the problem of low accuracy caused by insufficient data coverage of small samples, a fusion of shallow features and deep features is designed to enable the model to weighted fusion of shallow detail information and deep semantic information. Finally, by selecting different camera images in Market1501 dataset and DukeMTMC-reID dataset as small samples, respectively, and introducing another dataset data for joint training, we demonstrate the feasibility of this joint training framework, which can perform weakly supervised cross-domain Re-ID based on small sample data.

Human-in-the-loop cross-domain person re-identification

Human-in-the-loop cross-domain person re-identification

Cross-Domain Person Re-identification Combining Feature Concatenation and Attention

To improve the insufficient generalization and poor cross-domain capability of the existing direct cross-dataset person re-identification methods, a cross-domain person re-identification method combining feature concatenation and attention (FCANet) is proposed. The deep features of the network are concatenated to complement the feature information and obtain discriminatively feature, and the position attention module is introduced to enhance the data feature representation capability of the cross-domain task, using the joint training network of label smooth cross-entropy loss and triplet loss, model training in the source domain, and directly deploy to the target domain for testing. To verify the performance of the proposed method, it was experimented on three public datasets of Market1501, DukeMTMC-reID and MSMT17, which mAP and Rank1can reach 51.4% and 62.7% on Market1501. The results show that the proposed method has good performance in improving the generalization of cross-domain tasks, and the recognition accuracy outperforms the domain generalization algorithms of comparison.

Reducing Background Induced Domain Shift for Adaptive Person Re-Identification

Cross-domain person re-identification (Re-ID) is a challenging and important task in monitoring safety and procedure compliance of industrial work places. In this paper, a novel method is proposed to reduce background induced domain shift for adaptive person Re-ID. Specifically, a foreground-background joint clustering module is proposed to extract discriminative foreground and background features and an attention-based feature disentanglement module is designed to reduce the interference of background with the extraction of discriminative foreground features. Experimental results on three widely used person Re-ID benchmarking datasets (Market-1501, DukeMTMC-reID, and MSMT17) have demonstrated that the proposed method achieves promising performance compared with the state-of-the-art methods.

Dual semantic-aligned clustering for cross-domain person re-identification

Dual semantic-aligned clustering for cross-domain person re-identification

Local Correlation Ensemble with GCN Based on Attention Features for Cross-domain Person Re-ID

Person re-identification (Re-ID) has achieved great success in single-domain. However, it remains a challenging task to adapt a Re-ID model trained on one dataset to another one. Unsupervised domain adaption (UDA) was proposed to migrate a model from a labeled source domain to an unlabeled target domain. The main difference in the cross-domain is different background styles. Although the style transfer approach effectively reduces inter-domain gaps, it ignores the reduction of intra-class differences. Clustering-based pipelines maintain state-of-the-art performance for UDA by learning domain-independent features; however, most existing models do not sufficiently exploit the rich unlabeled samples in target domains due to unsatisfactory clustering. Thus, we propose a novel local correlation ensemble model that focuses on the diversity of intra-class information and the reliability of class centers. Specifically, a pedestrian attention module is proposed to enable the encoder to pay more attention to the person’s features to relieve interference caused by the shared background style. Furthermore, we propose a priority-distance graph convolutional network (PDGCN) module that employs a graph convolutional network network to predict the priority of a node as a class center and then calculates the distance between nodes with high priority values to screen out the class center nodes. Finally, the encoder features (local) and PDGCN features (context-aware) are combined to perform person Re-ID. The results of experiments on the large-scale public Re-ID datasets verified the effectiveness of the proposed method.

Learning to Adapt Across Dual Discrepancy for Cross-Domain Person Re-Identification.

Thanks to the advent of deep neural networks, recent years have witnessed rapid progress in person re-identification (re-ID). Deep-learning-based methods dominate the leadership of large-scale benchmarks, some of which even surpass the human-level performance. Despite their impressive performance under the single-domain setup, current fully-supervised re-ID models degrade significantly when transplanted to an unseen domain. According to the characteristics of the re-ID task, such degradation is mainly attributed to the dramatic variation within the target domain and the severe shift between the source and target domain, which we call dual discrepancy in this paper. To achieve a model that generalizes well to the target domain, it is desirable to take such dual discrepancy into account. In terms of the former issue, a prevailing solution is to enforce consistency between nearest-neighbors in the embedding space. However, we find that the search of neighbors is highly biased in our case due to the discrepancy across cameras. For this reason, we equip the vanilla neighborhood invariance approach with a camera-aware learning scheme. As for the latter issue, we propose a novel cross-domain mixup scheme. It works in conjunction with virtual prototypes which are employed to handle the disjoint label space between the two domains. In this way, we can realize the smooth transfer by introducing the interpolation between the two domains as a transition state. Extensive experiments on four public benchmarks demonstrate the superiority of our method. Without any auxiliary models and offline clustering procedure, it achieves competitive performance against existing state-of-the-art methods. The code is available at https://github.com/LuckyDC/generalizing-reid-improved.

Multi-level mutual supervision for cross-domain Person Re-identification

The challenges of cross-domain person re-identification mainly derive from two aspects: (1) The missing of target data labels. (2) The bias between source domain and target domain. Most of existing works focus on only one problem in the above two or deal with them separately. In this paper, we propose a new approach referred as to multi-level mutual supervision to achieve full utilization of labeled source data and unlabeled target data. Along this approach, we construct a dual-branch framework of which the upper branch is trained with original source data and target data while the lower branch is trained with augmented source data and target data. By applying common-pseudo-label and Maximum Mean Discrepancy (MMD) loss in our framework, the mutual supervision in multi levels is achieved. The results show that our model achieves SOTA performance on multiple popular benchmark datasets.

ICMiF: Interactive cascade microformers for cross-domain person re-identification

Person re-identification (ReID) arises in many applications, such as video surveillance and intelligent security. A challenge of cross-domain person ReID is the notorious distribution drift problem. Improving the accuracy of pseudo labels can promote the model to fit the target domain, so it is crucial to extract discriminative person features. The transformer can extract discriminative person features due to its ability to capture long distance dependencies, but its poor generalization ability may aggravate the influence of noise pseudo labels. The isolation between local information modeling and global information modeling further prevents the extraction of the discriminative person features. Therefore, this paper proposes an interactive cascade transformer framework. Firstly, a lightweight transformer structure (microformer) with better generalization is proposed. It maintains the advantages of traditional transformers on extracting discriminative features, and avoids the aggravation of the influence of noise labels. Secondly, an interactive cascade microformer framework (ICMiF) is proposed, which promotes the interaction between local information modeling and global information modeling to enhance the person feature representation. Local information facilitates mining global attribute dependencies, and global information improves the accuracy of local information. The experimental results demonstrate that the proposed ICMiF outperforms state-of-the-art methods for the cross-domain person ReID tasks.

Attention-calibration based double-branch cross-domain person re-identification

Existing cross-domain pedestrian re-identification methods tend to utilize domain adaptation or domain generalization strategies to eliminate the differences between domains, but these methods fail to fully characterize the characteristics of unknown domain samples, resulting in poor recognition performance in unknown domain. By learning specific features and invariant features of different domains, this paper proposes a new attention-calibration double-branch cross-domain pedestrian re-identification network (ACDBNet). Firstly, the attention calibration module is utilized to extract the channel information and spatial information of an image, and locate the distinguishable local features; then, the global branch and local branch are introduced to fully learn the domain specific feature and domain invariant feature respectively, and the features in these two branches are linearly weighted fused. Experimental results on a large number of public datasets demonstrate that the proposed method has excellent performance in various environments.

Cross-Modal Cross-Domain Dual Alignment Network for RGB-Infrared Person Re-Identification

RGB-Infrared cross-modal person re-identification (Re-ID) has drawn increasing attention due to its application value in practice. Most of the current works rely on a supervised training manner. However, in real-world applications, manual collection of pair-wise RGB-Infrared (IR) person data is labor-intensive and time-consuming. Moreover, when a trained model is directly used in another domain, there is usually a significant performance drop. To overcome the above problems, we make the first attempt to transfer the learned model to a new RGB-IR domain which is unlabeled. The practical problem covers two kinds of challenges, i.e., cross-modal (RGB-Infrared) and cross-domain (different dataset) person Re-ID. Previous works have often considered only one of them either cross-modal or cross-domain. In this work, we propose a dual alignment network (DAN) to solve the RGB-Infrared cross-modal cross-domain person Re-ID problem. This network consists of three parts: Domain Adversarial Alignment component (DAA), Pseudo Label Generation module for target domain (PLG), and Cross-Modal Alignment component (CMA). These three modules complement and promote the model to learn domain-invariant and modality-invariant person representations. Further, we propose a protocol of cross-modal cross-domain person Re-ID by synthesizing target domains by adding random noise, adjusting the lighting intensity, and changing the background color, respectively. Experiments on real and synthetic datasets under the same cross-modalities across domains demonstrate the effectiveness of our method.

Study of cross-domain person re-identification based on DCGAN

Study of cross-domain person re-identification based on DCGAN

Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification

Employing clustering strategy to assign unlabeled target images with pseudo labels has become a trend for person re-identification (re-ID) algorithms in domain adaptation. A potential limitation of these clustering-based methods is that they always tend to introduce noisy labels, which will undoubtedly hamper the performance of our re-ID system. To handle this limitation, an intuitive solution is to utilize collaborative training to purify the pseudo label quality. However, there exists a challenge that the complementarity of two networks, which inevitably share a high similarity, becomes weakened gradually as training process goes on; worse still, these approaches typically ignore to consider the self-discrepancy of intra-class relations. To address this issue, in this paper, we propose a multiple co-teaching framework for domain adaptive person re-ID, opening up a promising direction about self-discrepancy problem under unsupervised condition. On top of that, a mean-teaching mechanism is leveraged to enlarge the difference and discover more complementary features in target domain. Comprehensive experiments conducted on several large-scale datasets show that our method achieves competitive performance compared with the state-of-the-arts.

Inter-cluster and intra-cluster joint optimization for unsupervised cross-domain person re-identification

The purpose of unsupervised cross-domain (UCD) person re-identification (re-ID) is to adapt the well pre-trained model on the labeled source domain to the unlabeled target domain, which tackles a more realistic problem. Most successful UCD methods are devoted to exploring the relationship at instance level in the target domain, but neglect the relationship at cluster level. However, person re-ID is the task of retrieving samples belonging to the same identity. Thus, exploring the cluster level relationship is critical to improve the performance of re-ID. In this paper, a novel inter-cluster and intra-cluster joint optimization (IcJO) model is proposed. Specifically, for the inter-cluster relationship, a novel centroid-based contrastive learning strategy is designed, making the sample close to its cluster centroid and far away from the heterogeneous centroids. In order to alleviate the influence of clustering noise, a cluster-based uncertainty estimation coefficient is introduced to guide the contrastive learning process. As for the intra-cluster relationship, a centroid-based maximum mean discrepancy (cMMD) loss is adopted to narrow the intra-cluster distribution. Moreover, in order to enhance the feature invariance of each identity, a centroid-based similarity loss is developed. Extensive experiments are conducted on three standard benchmarks. The results demonstrate that our IcJO model performs favorably against the state-of-the-art UCD person re-ID methods.

A Hierarchical Processing and Completion Mechanism of Foreground Information for Person Re-Identification

Person re-identification (Re-ID) arises in many applications such as video surveillance and intelligent security. Background clutter and distribution drift are two issues that cross-domain person Re-ID faces. In this research, we propose that the background clutter problem be solved by combining semantic segmentation technology with human attribute identification technology. To overcome the distribution drift problem, we propose employing MMD as a metric for distribution differences and processing methods based on feature properties. The results of the experiments reveal that our strategy yielded the best results.

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.