Rank-1 Accuracy Research Articles

An enhanced Swin Transformer for soccer player reidentification

The re-identification (ReID) of objects in images is a widely studied topic in computer vision, with significant relevance to various applications. The ReID of players in broadcast videos of team sports is the focus of this study. We specifically focus on identifying the same player in images taken at any given moment during a game from various camera angles. This work varies from other person ReID apps since the same team wears very similar clothes, there are few samples for each identification, and image resolutions are low. One of the hardest parts of object ReID is robust feature representation extraction. Despite the great success of current convolutional neural network-based (CNN) methods, most studies only consider learning representations from images, neglecting long-range dependency. Transformer-based model studies are increasing and yielding encouraging results. Transformers still have trouble extracting features from small objects and visual cues. To address these issues, we enhanced the Swin Transformer with the levering of CNNs. We created a regional feature extraction Swin Transformer (RFES) backbone to increase local feature extraction and small-scale object feature extraction. We also use three loss functions to handle imbalanced data and highlight challenging situations. Re-ranking with k-reciprocal encoding was used in this study's retrieval phase, and its assessment findings were provided. Finally, we conducted experiments on the Market-1501 and SoccerNet-v3 ReID datasets. Experimental results show that the proposed re-ID method reaches rank-1 accuracy of 96.2% with mAP: 89.1 and rank-1 accuracy of 84.1% with mAP: 86.7 on the Market-1501 and SoccerNet-v3 datasets, respectively, outperforming the state-of-the-art approaches.

A Novel Lightweight Underwater Fish Individual Recognition Method Based on Local Feature Guidance Training for Precision Aquaculture

Highlights This article proposed a novel lightweight underwater fish individual recognition method. The proposed method can fully learn the characteristic information of fish. This method can guide training through local features. During prediction, only the global branch is utilized, resulting in a small number of parameters. Abstract. Individual recognition of fish within aquaculture fish groups is crucial for monitoring the growth status of each fish and is a key technology for precision and green aquaculture. However, accurately recognizing fish faces challenges due to the significant variations in fish postures and the influence of complex environments. This article aims to present a highly accurate method for underwater fish individual recognition, offering technical support for intelligent fish farming. To achieve this goal, a novel lightweight underwater fish individual recognition method based on local feature guidance training was proposed for precision aquaculture in this study. The method designs a multi-scale network based on deep learning and utilizes SE_ResNext for feature extraction. This network consists of a branch representing the global feature and a branch representing the local feature. During training, the full image of the fish individual is inputted as a coarse scale to the global branch (G-branch) to learn the global feature and calculate the ID loss. The image of each part formed after segmenting the fish individual is inputted as a fine-scale input to the partial branch (P-branch) to learn the local feature. Subsequently, the global feature and local feature are combined to form the fusion feature, and the Triplet loss and ID loss are calculated on the fusion feature. Finally, the loss calculated in the global feature and the loss calculated in the fusion feature are weighted and summed to obtain the total loss. Through the design of the loss function, the global feature is consistently influenced by the local feature, and the local feature serves to guide the global feature. During prediction, the global feature, having learned the information from the local feature, allows for the sole utilization of the G-branch to recognize fish individuals. This approach significantly reduces the number of parameters and computational requirements, resulting in a lightweight network. The method achieves high accuracy, with both Rank-1 and Rank-5 accuracy rates surpassing 95%. Remarkably, even with low image resolution and slight obscuration of the fish body, the Rank-1 accuracy remains above 90%. Keywords: Deep learning, Multi-scale network, Object detection, Recognition of fish individual.

Spatial-Temporal Federated Learning for Lifelong Person Re-identification on Distributed Edges

Data drift is a thorny challenge when deploying person re-identification (ReID) models into real-world devices, where the data distribution is significantly different from that of the training environment and keeps changing. To tackle this issue, we propose a federated spatial-temporal incremental learning approach, named FedSTIL, which leverages both lifelong learning and federated learning to continuously optimize models deployed on many distributed edge clients. Unlike previous efforts, FedSTIL aims to mine spatial-temporal correlations among the knowledge learnt from different edge clients. Specifically, the edge clients first periodically extract general representations of drifted data to optimize their local models. Then, the learnt knowledge from edge clients will be aggregated by centralized parameter server, where the knowledge will be selectively and attentively distilled from spatial- and temporal-dimension with carefully designed mechanisms. Finally, the distilled informative spatial-temporal knowledge will be sent back to correlated edge clients to further improve the recognition accuracy of each edge client with a lifelong learning method. Extensive experiments on a mixture of five real-world datasets demonstrate that our method outperforms others by nearly 4% in Rank-1 accuracy, while reducing communication cost by 62%. All implementation codes are publicly available on https://github.com/MSNLAB/Federated-Lifelong-Person-ReID.

Texture-guided transfer learning for low-quality face recognition.

Although many advanced works have achieved significant progress for face recognition with deep learning and large-scale face datasets, low-quality face recognition remains a challenging problem in real-word applications, especially for unconstrained surveillance scenes. We propose a texture-guided (TG) transfer learning approach under the knowledge distillation scheme to improve low-quality face recognition performance. Unlike existing methods in which distillation loss is built on forward propagation; e.g., the output logits and intermediate features, in this study, the backward propagation gradient texture is used. More specifically, the gradient texture of low-quality images is forced to be aligned to that of its high-quality counterpart to reduce the feature discrepancy between the high-and low-quality images. Moreover, attention is introduced to derive a soft-attention (SA) version of transfer learning, termed as SA-TG, to focus on informative regions. Experiments on the benchmark low-quality face DB's TinyFace and QMUL-SurFace confirmed the superiority of the proposed method, especially more than 6.3% Rank1 accuracy improvement is achieved on TinyFace.

Anchor Association Learning for Unsupervised Video Person Re-Identification.

Video-based person re-identification (re-id) has attracted a significant attention in recent years due to the increasing demand of video surveillance. However, existing methods are usually based on the supervised learning, which requires vast labeled identities across cameras and is not suitable for real scenes. Although some unsupervised approaches have been proposed for video re-id, their performance is far from satisfactory. In this article, we propose an unsupervised anchor association learning (UAAL) framework to address the video-based person re-id task, in which the feature representation of each sampled tracklet is regarded as an anchor. Specifically, we first propose an intracamera anchor association learning (IAAL) term that learns the discriminative anchor by utilizing the affiliation relations between an image and the anchors in each camera. Then, the exponential moving average (EMA) strategy is employed to update the anchor and the updated anchors are stored into an anchor memory module. On top of that, a cross-camera anchor association learning (CAAL) term is introduced to mine potential positive anchor pairs across cameras by presenting a cyclic ranking anchor alignment and threshold filtering method. Extensive experiments conducted on two public datasets show the superiority of the proposed method; for example, our method achieves 73.2% for rank-1 accuracy and 60.1% for mean average precision (mAP) score, respectively, on MARS, similarly 89.7% and 87.0% on DukeMTMC-VideoReID.

CLASH: Complementary Learning with Neural Architecture Search for Gait Recognition.

Gait recognition, which aims at identifying individuals by their walking patterns, has achieved great success based on silhouette. The binary silhouette sequence encodes the walking pattern within the sparse boundary representation. Therefore, most pixels in the silhouette are under-sensitive to the walking pattern since the sparse boundary lacks dense spatial-temporal information, which is suitable to be represented with dense texture. To enhance the sensitivity to the walking pattern while maintaining the robustness of recognition, we present a Complementary Learning with neural Architecture SearcH (CLASH) framework, consisting of walking pattern sensitive gait descriptor named dense spatial-temporal field (DSTF) and neural architecture search based complementary learning (NCL). Specifically, DSTF transforms the representation from the sparse binary boundary into the dense distance-based texture, which is sensitive to the walking pattern at the pixel level. Further, NCL presents a task-specific search space for complementary learning, which mutually complements the sensitivity of DSTF and the robustness of the silhouette to represent the walking pattern effectively. Extensive experiments demonstrate the effectiveness of the proposed methods under both in-the-lab and in-the-wild scenarios. On CASIA-B, we achieve rank-1 accuracy of 98.8%, 96.5%, and 89.3% under three conditions. On OU-MVLP, we achieve rank-1 accuracy of 91.9%. Under the latest in-the-wild datasets, we outperform the latest silhouette-based methods by 16.3% and 19.7% on Gait3D and GREW, respectively.

Gait recognition based on multi-feature representation and temporal modeling of periodic parts

Despite the ability of 3D convolutional methods to extract spatio-temporal information simultaneously, they also increase parameter redundancy and computational and storage costs. Previous work that has utilized the 2D convolution method has approached the problem in one of two ways: either using the entire body sequence as input to extract global features or dividing the body sequence into several parts to extract local features. However, global information tends to overlook detailed information specific to each body part, while local information fails to capture relationships between local regions. Therefore, this study proposes a new framework for constructing spatio-temporal representations, which involves extracting and fusing features in a novel manner. To achieve this, we introduce the multi-feature extraction-fusion (MFEF) module, which includes two branches: each branch extracts global features or local features individually, after which they are fused using multiple strategies. Additionally, as gait is a periodic action and different body parts contribute unequally to recognition during each cycle, we propose the periodic temporal feature modeling (PTFM) module, which extracts temporal features from adjacent frame parts during the complete gait cycle, based on the fused features. Furthermore, to capture fine-grained information specific to each body part, our framework utilizes multiple parallel PTFMs to correspond with each body part. We conducted a comprehensive experimental study on the widely used public dataset CASIA-B. Results indicate that the proposed approach achieved an average rank-1 accuracy of 97.2% in normal walking conditions, 92.3% while carrying a bag during walking, and 80.5% while wearing a jacket during walking.

Two-Stage Progressive Learning for Vehicle Re-Identification in Variable Illumination Conditions

Vehicle matching in variable illumination environments can be challenging due to the heavy dependence of vehicle appearance on lighting conditions. To address this issue, we propose a two-stage progressive learning (TSPL) framework. In the first stage, illumination-aware metric learning is enforced using a two-branch network via two illumination-specific feature spaces, used to explicitly model differences in lighting. In the second stage, discriminative feature learning is introduced to extract distinguishing features from a given vehicle. This process consists of a local feature extraction attention module, a local constraint, and a balanced sampling strategy. During the metric learning phase, the model expresses the union of local features, extracted from the attention module, with illumination-specific global features to form joint vehicle features. As part of the study, we construct a large-scale dataset, termed VERI-DAN (vehicle re-identification across day and night), to address the current lack of vehicle datasets exhibiting variable lighting conditions. This set is composed of 200,004 images from 16,654 vehicles, collected in various natural illumination environments. Validation experiments conducted with the VERI-DAN and Vehicle-1M datasets demonstrated that our proposed methodology effectively improved vehicle re-identification Rank-1 accuracy.

Fish Recognition in the Underwater Environment Using an Improved ArcFace Loss for Precision Aquaculture

Accurate fish individual recognition is one of the critical technologies for large-scale fishery farming when trying to achieve accurate, green farming and sustainable development. It is an essential link for aquaculture to move toward automation and intelligence. However, existing fish individual data collection methods cannot cope with the interference of light, blur, and pose in the natural underwater environment, which makes the captured fish individual images of poor quality. These low-quality images can cause significant interference with the training of recognition networks. In order to solve the above problems, this paper proposes an underwater fish individual recognition method (FishFace) that combines data quality assessment and loss weighting. First, we introduce the Gem pooing and quality evaluation module, which is based on EfficientNet. This module is an improved fish recognition network that can evaluate the quality of fish images well, and it does not need additional labels; second, we propose a new loss function, FishFace Loss, which will weigh the loss according to the quality of the image so that the model focuses more on recognizable fish images, and less on images that are difficult to recognize. Finally, we collect a dataset for fish individual recognition (WideFish), which contains and annotates 5000 images of 300 fish. The experimental results show that, compared with the state-of-the-art individual recognition methods, Rank1 accuracy is improved by 2.60% and 3.12% on the public dataset DlouFish and the proposed WideFish dataset, respectively.

Cyclic style generative adversarial network for near infrared and visible light face recognition

Near Infrared and Visible Light (NIR-VIS) face recognition attracts attention from researchers because of its potential for safety, illumination invariance, and stability. Nevertheless, the difference between NIR and VIS domains, the domain gap, remains a huge problem for matching NIR and VIS images. Specifically, for the same identity, the appearance in the NIR domain is different from the VIS domain. Thus, traditional face recognition methods cannot be effective. To address this problem, this paper proposes a novel model, called Cyclic-Style GAN (CS-GAN). First, the pre-trained Style-GAN 3 network is embedded into the Cycle-GAN structure for NIR-VIS cross-domain learning. Second, there is a cyclic subspace learning method consisting of latent loss and style loss, through which both style (domain feature) and facial characteristic features are learned to improve the quality of synthesized images. The model synthesizes realistic VIS images from NIR images and does the face recognition task in the VIS domain. The proposed method achieves 99.6% Rank-1 accuracy on the CASIA NIR-VIS 2.0 database which is a state-of-the-art result. The visualization results show that the proposed model synthesizes VIS images with a clear texture of faces and in close-to-reality color.

GaitSG: Gait Recognition with SMPLs in Graph Structure.

Gait recognition aims to identify a person based on his unique walking pattern. Compared with silhouettes and skeletons, skinned multi-person linear (SMPL) models can simultaneously provide human pose and shape information and are robust to viewpoint and clothing variances. However, previous approaches have only considered SMPL parameters as a whole and are yet to explore their potential for gait recognition thoroughly. To address this problem, we concentrate on SMPL representations and propose a novel SMPL-based method named GaitSG for gait recognition, which takes SMPL parameters in the graph structure as input. Specifically, we represent the SMPL model as graph nodes and employ graph convolution techniques to effectively model the human model topology and generate discriminative gait features. Further, we utilize prior knowledge of the human body and elaborately design a novel part graph pooling block, PGPB, to encode viewpoint information explicitly. The PGPB also alleviates the physical distance-unaware limitation of the graph structure. Comprehensive experiments on public gait recognition datasets, Gait3D and CASIA-B, demonstrate that GaitSG can achieve better performance and faster convergence than existing model-based approaches. Specifically, compared with the baseline SMPLGait (3D only), our model achieves approximately twice the Rank-1 accuracy and requires three times fewer training iterations on Gait3D.

DGSN: Learning how to segment pedestrians from other datasets for occluded person re-identification

In this paper, we present three major challenges in occluded person Re-Identification (ReID): different occlusions, background interference, and dataset bias. To address the first and second challenges, our approach incorporates pedestrian segmentation to distinguish between pedestrian and non-pedestrian regions. Additionally, to tackle the third challenge, we introduce an effective image enhancement method called Enhanced Random Occluding (ERO). ERO leverages other datasets with segmentation annotations to compensate for the lack of detailed annotations in ReID datasets. We compare the effectiveness of ERO with existing methods. To utilize the prior knowledge obtained by ERO, we introduce the Priori Segmentation Module (PSM) and the Domain Generalization Module (DGM). The PSM module enables learning out-of-domain prior knowledge without relying on external networks, while the DGM module transfers this knowledge to the current domain. Finally, we utilize the obtained segmentation results as attention maps for feature aggregation. ERO, PSM, and DGM together constitute the Domain Generalization Segmentation Network (DGSN). Our experimental results on occluded and holistic person ReID benchmarks demonstrate the superiority of the proposed DGSN. On the Occluded-Duke dataset, we achieved a mAP of 69.9% (+2.0%) and a rank-1 accuracy of 60.7% (+0.3%), surpassing state-of-the-art methods significantly.

Research on Key Technology of Ship Re-Identification Based on the USV-UAV Collaboration

Distinguishing ship identities is critical in ensuring the safety and supervision of the marine agriculture and transportation industry. In this paper, we present a comprehensive investigation and validation of the progression of ship re-identification technology within a cooperative framework predominantly governed by UAVs. Our research revolves around the creation of a ship ReID dataset, the creation of a ship ReID dataset, the development of a feature extraction network, ranking optimization, and the establishment of a ship identity re-identification system built upon the collaboration of unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs). We introduce a ship ReID dataset named VesselID-700, comprising 56,069 images covering seven classes of typical ships. We also simulated the multi-angle acquisition state of UAVs to categorize the ship orientations within this dataset. To address the challenge of distinguishing between ships with small inter-class differences and large intra-class variations, we propose a fine-grained feature extraction network called FGFN. FGFN enhances the ResNet architecture with a self-attentive mechanism and generalized mean pooling. We also introduce a multi-task loss function that combines classification and triplet loss, incorporating hard sample mining. Ablation experiments on the VesselID-700 dataset demonstrate that the FGFN network achieves outstanding performance, with a Rank-1 accuracy of 89.78% and mAP of 65.72% at a state-of-the-art level. Generalization experiments on pedestrian and vehicle ReID datasets reveal that FGFN excels in recognizing other rigid body targets and diverse viewpoints. Furthermore, to further enhance the advantages of UAV-USV synergy in ship ReID performance, we propose a ranking optimization method based on the homologous fusion of multi-angle UAVs and heterologous fusion of USV-UAV collaborative architecture. This optimization leads to a significant 3% improvement in Rank-1 performance, accompanied by a 73% reduction in retrieval time cost.

Multi-proxy feature learning for robust fine-grained visual recognition

Visual representation for fine-grained visual recognition can be learned by mandatorily enforcing all samples of the same category into a uniform representation. This strict training objective performs well under closed-set setting but is not applicable to data in the wild containing noisy annotations and long-tailed distributions, e.g., it may lead to a feature space biased to head categories. This paper tackles this challenge by pursuing a more balanced and discriminative feature space by first retaining intra-class variances to isolate noises, then eliminating intra-class variances to improve the visual recognition performance. We propose the Compact Memory Updater to maintain a memory bank, which memorizes proxy features to represent multiple typical appearances of each category in the training set. The Proxy-based Feature Enhancement hence leverages proxy features to ensure samples of the same category have similar features. Iteratively running those two modules boosts the robustness and discriminative power of the learnt representation, hence facilitates various fine-grained visual recognition tasks including person re-identification (re-id), image classification and retrieval. Extensive experiments on noisy and long-tailed training sets show this Multi-Proxy Feature Learning (MPFL) framework achieves promising performance. For instance on a training set with 90% one-shot categories, MPFL outperforms the recent long-tailed person re-id method LEAP-AF by 16.9% in rank-1 accuracy.

GAIT Technology for Human Recognition using CNN

Gait is a distinctive biometric characteristic that can be detected from a distance; as a result, it has several uses in social security, forensic identification, and crime prevention. Existing gait identification techniques use a gait template, which makes it difficult to keep temporal information, or a gait sequence, which maintains pointless sequential limitations and loses the ability to portray a gait. Our technique, which is based on this deep set viewpoint, is immune to frame permutations and can seamlessly combine frames from many videos that were taken in various contexts, such as diversified watching, angles, various outfits, or various situations for transporting something. According to experiments, our single-model strategy obtains an average rank-1 accuracy of 96.1% on the CASIA-B gait dataset and an accuracy of 87.9% on the OU-MVLP gait dataset when used under typical walking conditions. Our model also demonstrates a great degree of robustness under numerous challenging circumstances. When carrying bags and wearing a coat while walking, it obtains accuracy on the CASIA-B of 90.8% and 70.3%, respectively, greatly surpassing the best approach currently in use. Additionally, the suggested method achieves a satisfactory level of accuracy even when there are few frames available in the test samples; for instance, it achieves 85.0% on the CASIA-B even with only 7 frames.

A feature enhancement loss for person re-identification

The goal of person re-identification (ReID) is to recognize the same person across cameras. Classification loss is one of the most widely used objective functions in person ReID tasks based on deep learning. However, the features, which are learned with the classification loss, are not sufficiently discriminative enough when they are close to the origin. In this study, we propose a feature enhancement loss to move features of person images away from the origin. During training, our proposed method adds a regularization item to avoid the feature vector near the origin point. Our method was evaluated on two benchmark person ReID benchmark datasets, Market1501 and DukeMTMC-reID. Results show that the proposed method outperforms the state-of-the-art method by 0.9% and 1.2% on rank-1 accuracy and mean average precision (mAP) index on Market-1501, 1.0% and 1.4% on rank-1 accuracy and mAP index on DukeMTMC-reID. This means that when learning features with a classification loss, making the features far away from the origin point is meaningful.

Omni-Domain Feature Extraction Method for Gait Recognition

As a biological feature with strong spatio-temporal correlation, the current difficulty of gait recognition lies in the interference of covariates (viewpoint, clothing, etc.) in feature extraction. In order to weaken the influence of extrinsic variable changes, we propose an interval frame sampling method to capture more information about joint dynamic changes, and an Omni-Domain Feature Extraction Network. The Omni-Domain Feature Extraction Network consists of three main modules: (1) Temporal-Sensitive Feature Extractor: injects key gait temporal information into shallow spatial features to improve spatio-temporal correlation. (2) Dynamic Motion Capture: extracts temporal features of different motion and assign weights adaptively. (3) Omni-Domain Feature Balance Module: balances fine-grained spatio-temporal features, highlight decisive spatio-temporal features. Extensive experiments were conducted on two commonly used public gait datasets, showing that our method has good performance and generalization ability. In CASIA-B, we achieved an average rank-1 accuracy of 94.2% under three walking conditions. In OU-MVLP, we achieved a rank-1 accuracy of 90.5%.

Regional Time-Series Coding Network and Multi-View Image Generation Network for Short-Time Gait Recognition.

Gait recognition is one of the important research directions of biometric authentication technology. However, in practical applications, the original gait data is often short, and a long and complete gait video is required for successful recognition. Also, the gait images from different views have a great influence on the recognition effect. To address the above problems, we designed a gait data generation network for expanding the cross-view image data required for gait recognition, which provides sufficient data input for feature extraction branching with gait silhouette as the criterion. In addition, we propose a gait motion feature extraction network based on regional time-series coding. By independently time-series coding the joint motion data within different regions of the body, and then combining the time-series data features of each region with secondary coding, we obtain the unique motion relationships between regions of the body. Finally, bilinear matrix decomposition pooling is used to fuse spatial silhouette features and motion time-series features to obtain complete gait recognition under shorter time-length video input. We use the OUMVLP-Pose and CASIA-B datasets to validate the silhouette image branching and motion time-series branching, respectively, and employ evaluation metrics such as IS entropy value and Rank-1 accuracy to demonstrate the effectiveness of our design network. Finally, we also collect gait-motion data in the real world and test them in a complete two-branch fusion network. The experimental results show that the network we designed can effectively extract the time-series features of human motion and achieve the expansion of multi-view gait data. The real-world tests also prove that our designed method has good results and feasibility in the problem of gait recognition with short-time video as input data.

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.

Combined visual and spatial-temporal information for appearance change person re-identification

Person re-identification (ReID) seeks to identify the same individual across different cameras by matching their corresponding images. The current ReID datasets are limited in size and diversity, especially in terms of clothing changes, making traditional techniques vulnerable to appearance variations. Further, current approaches rely heavily on appearance features for discrimination, which is unreliable when a person’s appearance changes. We hypothesize that the ReID accuracy can be enhanced by training the ReID model on a large volume of diversified training data and combining multiple features for discrimination. We use the image channel shuffling data augmentation method, producing a large volume of diversified training data. Also, a two-stream visual and spatial-temporal method is proposed to learn the feasible features for appearance change scenarios. The appearance features obtained from the visual stream are combined with spatio-temporal information to discriminate between two people. The proposed approach is evaluated for its robustness on short-term and on long-term datasets. The presented two-stream approach outperforms earlier methods by achieving Rank1 accuracy of 98.6% on Market1501, 95.52% on DukeMTMC-reID, 76.21% on LTCC, and 91.76% on VC-Clothes, respectively.