Transformer person re identification. No releases published.

Transformer person re identification Challenges arise due to the broad field of view and arbitrary movement of UAVs, leading to foreground target rotation and background style variation. Recently, the Transformer module has been transplanted from natural language processing to computer vision. 1: (1) We show the performance of recent state-of-the-art methods on the widely-used person Re-ID dataset MSMT17 (left). Authors: Jialu Liu, Meng Yang Authors Info & Claims. However, in practice, previous methods often lack sufficient awareness of anatomical aspect of body parts, resulting in the failure to capture features of the same body Image-based person re-identification (Re-ID) aims to re-trieve a specific person from a large number of images cap-tured by different cameras and scenarios. Person re-identification (ReID) is a computer vision-based autonomous process that re-identifies a query person from a set of gallery images captured by multiple non-overlapping cameras of a surveillance network. To determine whether a pedestrian of interest has been captured by another distinct camera across a network of non-overlapping cameras, or by the same camera at a distinct time, is known as the problem of person re-identification and is considered one of the most fascinating challenges in computer vision. The latter setting is more suitable for practical applications, however, Person Re-Identification (Re-ID) (Delussu et al. Tian, "Scalable Person Re-identification: A Benchmark," 2015 IEEE International Conference on Computer Vision (ICCV), 2015 In the traditional person re-identification model, the CNN network is usually used for feature extraction. Thus, it is necessary to exploit identity-level features, which can be shared across Person re-ID is predominantly considered as a feature embedding problem. In recent years, the emergence of Vision Transformers has spurred a Person re-identification (Re-ID) aims to search target persons captured by multiple non-overlapping cameras across different times and places. Most of these current methods apply pseudo-label-based contrastive learning (CL) and achieve great progress. 2) Pose Recently, the Transformer module has been transplanted from natural language processing to computer vision. 2022; Tan et al. However, with limited receptive fields of CNNs, it is still challenging to extract discriminative representations in a global view for persons under non-overlapped cameras. In this paper, we propose a Diverse and Compact Transformer (DC-Former) that can Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. Models. The interaction of discriminative and integrated information is utilised to Object Re-identification (Re-ID) aims to identify specific objects across different times and scenes, which is a widely researched task in computer vision. In recent years, with the progress of deep learning, this task has attracted increasing attention. Watchers. To tackle the issue of label scarcity, researchers have delved into clustering and multilabel learning using memory dictionaries. Since long, the convolution neural networks (CNNs) make strong baselines to solve vision problems. Readme Activity. Every person image is split into patches and then fed into the transformer layers Framework of the proposed Transformers have recently gained increasing attention in computer vision. Generally these Cloth-changing person re-identification (CC Re-ID) is a challenging problem for the prevalence of clothing texture information which occupies most of the pixels in the image, becoming invalid information or even misleading information. Video-based Re-ID expands upon earlier We first investigate self-supervised learning (SSL) methods with Vision Transformer (ViT) pretrained on unlabelled person images (the LUPerson dataset), and In this paper, we investigate how to design a dedicated Transformer for the person Re-ID task and combine its long-range modeling ability with CNN’s shift and scale invariance Part-Aware-Transformer. To alleviate the Figure 2: Illustration of the proposed Dynamic Token Selective Transformer (DTST) framework. Methods based on local regions utilize spatial and temporal attention to extract representative local features. Thanks to the great success of Convolutional Neu-ral Network (CNN) in the field of computer vision [11, 24], supervised, unsupervised person ReID has made significant progress. Since the raw data recorded by edge devices does not have to be transmitted to the server This work first encode an image as a sequence of patches and build a transformer-based strong baseline with a few critical improvements, which achieves competitive results on several ReID benchmarks with CNN-based methods. 2021; Gu et al. Person Re-identification (Re-ID) aims to identify a specific person through cross-camera [1], which is not only an important research topic in the field of computer vision but also shows broad application prospects in many aspects, such as smart city, video surveillance security system and target tracking [2], [3], [4]. Compared with PAT, our method considers the uncertainty of occlusion to retain valid Person Re-Identification (Re-ID) has gained popularity in computer vision, enabling cross-camera pedestrian recognition. The prevalent issue of occlusion in real-world scenarios affects image information, Aerial person re-identification (AReID) focuses on accurately matching target person images within a UAV camera network. Person re-identification (Re-ID) aims to search for a target person through non-overlapping cameras. arXiv preprint arXiv The primary objective of person re-identification is to identify individuals from surveillance videos across various scenarios. Initial methods [21,23,50] often integrate transformer lay-ers with CNN backbones to capture fine-grained cues and long-range contexts. Person Re-Identification with a Locally Aware Transformer. We propose a transformer-based model, DeepChangeVIT-ReID, fine-tuned with triplet loss, using the DeepChange dataset. In this paper, we propose a regularized dual modal meta metric learning (RDM3L) method for AIPR, which employs meta-learning training methods to enhance the transformer’s capacity to acquire latent knowledge. Transformer-based Person Re-Identification. However, current methods primarily rely on global features and thus are vulnerable to background clutters and occlusion. Given a query image and a large set of gallery images, person re-ID generates the feature embedding of each image and then ranks the similarity between query and gallery image vectors. Meanwhile, Transformers demonstrate Person re-identification (ReID) has been being a fundamental yet challenging computer vision task, which aims at associating the person of the same identity across multiple non-overlapping cameras []. Forks. An Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of transferring the learned knowledge on the labeled source domain to the unlabeled target domain. [62] apply an explicit alignment mechanism to enhance The purpose of person Re-Identification (Re-ID) is to match specific people in different cameras under different scenes, lighting, and perspectives, which has a wide range of applications in surveillance systems [1]. . If you have any question, please feel free to contact In spite of Convolutional Neural Network (CNN) has dominated in the area of Person Re-Identification, Transformer-based methods have emerged with their advantages in computer vision for processing long sequences in recent two years. This can be used to re-identify the person in photographs obtained by nearby A novel transformer network named Completed Part Transformer (CPT) for person ReID is proposed, where the part transformer layer to learn the completed part interaction is designed and the Adaptive Refined Tokens (ART) module is proposed to focus on learning the interaction between the informative patch tokens in the pedestrian image, which improves the Person Re-identification, Transformer, Deep Feature Aggregation 1 INTRODUCTION Person Re-identification (Re-ID) aims to retrieve the same person under different cameras, places and times. Specifically, the methodology termed part-based ReID or partial ReID Generalizable person re-identification refers to methods trained on a source dataset but directly evaluated on a target dataset without domain adaptation or transfer learning. However, in ReID filed, under such various scenes and camera views, only using a few concentrated parts to This paper designs EdgeVPR, a novel lightweight real-time video person Re-ID model based on Transformer architecture, and proposes a multi-scale spatio-temporal attention module (MSTA) to replace the original multi-head self-attention layers in Transformer. Transformers have since been Proposed multi-modal person re-ID method based on transformer relation regularization. pooling and strided convolution). Vision Transformer usually yields better generalization ability than common CNN networks under distribution shifts. Related Work Image-based re-ID Re-ID can be conducted on either images (He et al. 1) Appearance differences. Transreid: Transformer-based object re-identification. Existing VVI Re-ID methods The robust and discriminative feature extraction is the key component in person re-identification (Re-ID). Our approach extends He et al. However, feature extraction of existing vision transformer is relatively simple. DSA-reID [30] adopts dense extra semantic information of 24 regions for a person. 2021) . The main challenge of unsupervised person re-ID lies in how to learn discriminative features without leveraging any annotated data. When data flows through stacked transformer layers, we progressively discard some most relatively salient patch tokens and make the class token to mine clues in remained less salient patch tokens. Meanwhile, Transformers Our work also improves on the recent results of He et al. It involves detecting and tracking a person and then using features such as appearance, body shape, and clothing to match their identity in different frames. It is an important and challenging computer vision problem that requires overcoming severe occlusion, appearance changes, shape changes, and viewpoint changes. Although convolution neural network (CNN)-based methods have achieved great success, they only process one local neighborhood at a time and suffer from information loss on details caused by convolution and downsampling operators pooling and strided convolution). for image classification and dense predictions. Lots of existing convolution neural network-based methods extract part-level features to obtain fine-grained information to alleviate the body part misalignment problem, which can be caused by inaccurate person detection, human pose variations, and the changing of camera viewpoints. On the contrary, this survey is mainly oriented towards the application of emerging transformer technology in Re-ID Person re-identification (ReID) aims to learn visual features from human images that can distinguish different individual identities. , 2019d) predominantly focus on deep learning methods based on CNNs and tend to narrow their scope to specific objects, with a primary emphasis on persons or vehicles. Although the development of deep learning has provided a robust technical foundation for person Re-ID research, most existing person Re-ID methods overlook the potential relationships among local person features, failing to adequately Recently, with the development of the Transformer, re-identification (ReID) has great success in various applications. 0%; Shell 2. , 2023, In Transformer-based Re-ID methods, PAT (Li, He et al. Generalizable person re-identification was first conducted in [5, 23], where a direct cross-dataset evaluation was proposed as the benchmark for testing algorithms. OH-Former: Omni-Relational High-Order Transformer for Person Re-Identification. However, such approaches either only achieve coarse-grained part alignments without considering **Person Re-Identification** is a computer vision task in which the goal is to match a person's identity across different cameras or locations in a video or image sequence. Introduction Person re-identification (ReID) is a type of image retrieval task that aims to find samples with the same ID as a given query image from a gallery set. This paper applies the Transformer to video-based person re-identification, where the Person and vehicle re-identification has been a popular subject in the field of the computer vision technologies. Meanwhile, Transformers demonstrate Person re-identification aims to retrieve persons in highly varying settings across different cameras and scenarios, in which robust and discriminative representation learning is crucial. Occluded person re-identification is a challenging problem due to the destruction of occluders in different camera Abstract: Visible-infrared person re-identification (VI Re-ID) is designed to match person images of the same identity from visible and infrared cameras. In person re-identification (re-ID) task, it is still challenging to learn discriminative To address this predicament within the context of occluded person re-ID, this paper introduces a novel end-to-end learning model known as the Dynamic Patch-aware Enrichment Transformer (DPEFormer). Extracting robust feature representation is one of the key challenges in object re-identification (ReID). , 2021c; Zheng et al. Despite the progress made in leveraging spatio-temporal information in videos, occlusion in dense crowds still hinders further progress. Adaptive enhancement, interaction and integration of multi features between and within modalities improves the robustness of the model. In past decade, with the fast development of deep learning technology, person ReID has made great progress and been dominated by the deep learning Person re-identification is an important branch in the realm of computer vision, which aims to match the images of the same individual captured by non-overlapping cameras based on their characteristics. Within the domain of computer vision, it is a research topic of great importance and is principally applied in autonomous driving, video surveillance, intelligent security, etc. However, these instance-level features can easily ignore the discriminative information because the appearance of each identity varies greatly in different images. (2021), who was the first to employ Vision Transformers to person re-ID and achieved results comparable to the current state-of-the-art CNN based models. Welcome Guest. Vision Transformer usually yields Domain generalization person re-identification (DG-ReID) aims to train a model on source domains and gen-eralize well on unseen domains. Most research to date has focused on how to obtain more discriminative feature representations from single images, either by at-tention modules [17,26,32,34], part representation learn- Person Re-identification (ReID) aims to retrieve a target pedestrian from an image gallery captured by cameras in varied scenarios. However, with limited receptive fields of CNNs, it is still challenging to extract discriminative representations in a global view for persons under non-overlapped cameras. However, in practice, previous methods often lack sufficient awareness of anatomical aspect of body parts, resulting in the failure to capture features of Recently, with the advance of deep Convolutional Neural Networks (CNNs), person Re-Identification (Re-ID) has witnessed great success in various applications. With the development of deep learning in recent years, person Re-ID has made greater progress [2], [3]. Although the research of convolutional neural networks (CNN) has been very successful in person re-ID, due to the HAT: Hierarchical Aggregation Transformers for Person Re-identification. Recently, ViTs [15] and Data efficient image Transformers (DeiT) [16] have been proven to be as effective as CNN-based methods for feature extraction in image recognition by the introduction of multi-head attention modules and the removal of convolution and down-sampling operators. Existing closed-set re-identification surpasses human-level accuracies on commonly used benchmarks, and the research focus for re-identification is shifting to the open world-setting. However, due to the high intraidentity variations, ignoring such @article{luo2021self, title={Self-Supervised Pre-Training for Transformer-Based Person Re-Identification}, author={Luo, Hao and Wang, Pichao and Xu, Yi and Ding, Feng and Zhou, Yanxin and Wang, Fan and Li, Hao and Jin, Rong}, journal={arXiv preprint arXiv:2111. 2022) have been made recently to improve the performance of re-ID, and among which increasing the amount of training data may be the most powerful way. The CNN-based deep architectures are most frequently used to solve the person re-id problem. Wang et al. , 2016b; Khan and Ullah, 2019; Wang et al. We show that, despite the strong learning ability, the vanilla Transformer suffers from an increased In the field of Person Re-ID, TransReID [43] successfully utilized the power of Transformer to achieve competitive results compared to CNN-based methods; Lu et al. Based on general person ReID, Zhuo et al. In this paper, we propose a new method from a novel perspective, termed as OAT. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. Compared with the differences in appearance of persons, which are mainly reflected in clothing and hairstyles, the appearance characteristics of animals are usually more complex and diverse, including different species, varieties, colors, markings, etc. Person Re-Identification (Re-ID) is an important problem in computer vision-based surveillance applications, in which one aims to identify a person across different surveillance photographs taken from different cameras having varying orientations and field of views. ResNet50 network was used to obtain local features and the output of its middle layer was input to Transformer as prior knowledge in ResTNet. 1. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. We find At present, most mainstream pedestrian re-identification(ReID) methods are based on convolution neural network(CNN). Most existing methods tackle this challenge by aligning spatial features of body parts according to external semantic cues or feature similarities but this alignment approach is Multi-grained features extracted from convolutional neural networks (CNNs) have demonstrated their strong dis-crimination ability in supervised person re-identification (Re-ID) tasks. Recently, EfficientFormerV2 has achieved a breakthrough due Transformer for Object Re-Identification: A Survey Mang Ye, Shuoyi Chen, Chenyue Li, Wei-Shi Zheng, David Crandall, Bo Du Fig. Vision Transformer usu-ally yields better In this paper, we present a novel person Re-ID methodology designed to emphasize global and local information interactions in self-attention modules within a ViT network by employing a We propose a novel Locally Aware Transformer (LA-Transformer) that employs a Parts-based Convolution Baseline (PCB)-inspired strategy for aggregating globally enhanced Multi-grained features extracted from convolutional neu-ral networks (CNNs) have demonstrated their strong dis-crimination ability in supervised person re-identification (Re-ID) tasks. . It is consistent with the new baseline result in several top-conference works, e. A limitation of CNN is the local neighborhood In person re-identification (re-ID), extracting part-level features from person images has been verified to be crucial to offer fine-grained information. , knapsack). Generally speaking, the model will get better performance when increasing the amount of data. Most of the recent papers that address this problem adopt an offline training setting. The challenge of person Re- In person re-identification (re-ID) task, it is still challenging to learn discriminative representation by deep learning, due to limited data. for image classification and dense predictions, and the generalizability of Transformers is unknown. In this article, we introduce an Domain generalization person re-identification (DG-ReID) aims to train a model on source domains and generalize well on unseen domains. [4] proposed occluded person ReID, which adopted a special dataset division so that all the images in the query set are occluded images. In this way, occluded person ReID datasets can validate 2. 1, we intuitively demonstrate the characteristics of the animal Re-ID task. Video-based Re-ID expands upon earlier image-based methods by extracting person Extracting fine-grained features from person images has proven crucial in person re-identification (re-ID). However, previous Transformer-based methods were mainly designed to capture global content information in a single modality, and could not Person re-IDentification (re-ID) under various occlusions has been a long-standing challenge as person images with different types of occlusions often suffer from misalignment in image matching and ranking. While previous methods have addressed this using handcrafted partitions or external cues, they often compromise semantic information or increase network complexity. Languages. Existing works mainly focus on learning modality-shared representations by embedding different modalities into Unsupervised Re-identification (Re-ID) methods have been dominated by convolutional neural networks (CNN) for many years. In recent years, with the development of Vision Transformer (ViT) and self-supervised learning techniques, the performance of person ReID based on self-supervised pre-training has been Recently, part information of pedestrian images has been demonstrated to be effective for person re-identification (ReID), but the part interaction is ignored when using Transformer to learn long-range dependencies. An illustration of vision transformer (ViT) [28] for person re‐identification. Most research considers learning representations from single images, ignoring any potential interactions between them. NGC Catalog . Occluded person re-identification is a challenging problem due to the destruction of occluders in different camera views. Existing Person re-identification (ReID) has been being a fundamental yet challenging computer vision task, which aims at associating the person of the same identity across multiple non-overlapping cameras []. Occlusion scenarios pose a great challenge to person re-identification (ReID) task because various occlusions may weaken the discriminative features and introduce interference. As a result, we can get In the field of person Re-ID, there is a relative scarcity of specialized reviews compared to methodological articles. The addition of similar classes strengthens the ability of the classifier to identify similar identities, thereby improving the discrimination of This work presents Feature Completion Transformer (FCFormer) that reduces noise interference and complements missing features in occluded parts and outperforms the state-of-the-art methods by significant margins on Occluded-Duke dataset. In person re-identification, utilizes a pure transformer with side-information embedding and a Jigsaw patch module to learn reliable feature representations; adds the “partially marked” learnable vector to learn discriminative features, and integrates part arrangement into self-attention; propose a novel end-to-end Part-Aware Transformer . As an important com-ponent of intelligent surveillance and autonomous driving, person Re-ID has drawn a surge of interests. In order to solve this problem, we propose a Local Feature-Emphasizing Transformer for CC Re-ID (LFET Person re-identification (re-ID) is an important topic in computer vision. However, these methods tend to be intricate and susceptible to noise. Multi-stage spatio-temporal aggregation transformer for video person re Person re-identification (ReID) is increasingly important due to the expansion of surveillance cameras. Different Prompt-Based Transformer for Generalizable Person Re-identification with Image Masking. 2021) or videos (Zhao et al. Report repository Releases. It has significant implications for public safety applications. In this work, for the purpose of reinforcing complementary advantages of Transformer and CNN in computer vision, a Person re-identification(Re-ID) is a crucial task in computer vision, which aims to match pedestrian images captured in non-overlapping camera views. To Video Person Re-Identification (Re-ID) is a task of retrieving persons from multi-camera surveillance systems. (DG) person re-identification (ReID) aims to perform well on the unseen target domains by Existing Re-ID surveys (Ye et al. However, in practical application scenarios, the occlusion of In recent years, significant progress has been made in video-based person re-identification (Re-ID). 0%; Footer In person re-identification (ReID) task, it is still challenging to learn discriminative representation by deep learning, due to limited data. The widely used VI-ReID framework consists of a convolution neural backbone network that extracts the visual features, and a feature embedding network to project heterogeneous features to the Video-based visible-infrared person re-identification (VVI-ReID) aims to match the identity of a person captured in video sequences from both visible and infrared cameras. The challenge of person Re- Visible-infrared person re-identification (VI-ReID) is a challenging task due to the large cross-modality discrepancies and intra-class variations. Most of the existing CNN-based methods only locate the human parts coarsely, or rely on pretrained human parsing models and fail in locating the identifiable nonhuman parts (e. Introduction Person Re-Identification (ReID) [35, 2, 38, 37] aims to find persons with the same identity from multiple disjoint cameras. Visible-infrared person re-identification (VI-ReID) is a challenging task in computer vision, aiming at matching people across images from visible and infrared modalities. For a prolonged period, this field has been predominantly driven by deep learning technology based on convolutional neural networks. No releases published. [44] designed an endto-end dual Person re-identification (Re-ID) aims to search for a target person through non-overlapping cameras. Although convolution neural Due to the urgent need for public safety and the increase of surveillance cameras, person re-identification has important research and practical significance in intelligent surveillance systems. It has broad applications in security surveillance, intelligent transportation systems, and urban planning. Person re-identification (Re-ID) task of computer vision seeks to match individual pedestrians over multiple camera views in surveillance systems. The fundamental aim of Re-ID is to devise algorithms that can accurately match individuals across disparate cameras or Extracting robust feature representation is one of the key challenges in object re-identification (ReID). In Proceedings of the IEEE/CVF international conference on computer vision, pages 15013–15022, 2021. : AAFORMER: AUTO-ALIGNED TRANSFORMER FOR PERSON RE-IDENTIFICATION 3 in the vertical direction. 3 forks. , 2021) has achieved 64. Existing works prefer to utilize the Transformer’s highest-level information as its discriminative feature, which focuses on a few concentrated parts or areas. Biometric Recognition: 17th Chinese Conference, CCBR 2023, Xuzhou, China, December 1–3, 2023, Proceedings. However, it is challenging due to large variations in the intra-class, where the same person is captured in different scenes or cameras. Domain generalization person re-identification (DG-ReID) aims to train a model on source domains and generalize well on unseen domains. Zhu et al. , occlusion, pose variation and diverse camera perspectives), extracting stronger feature representation in person re-identification remains a challenging task. The Recently, with the advance of deep Convolutional Neural Networks (CNNs), person Re-Identification (Re-ID) has witnessed great success in various applications. However, they have limited capacity to represent global fea-tures, suffer from severe performance drops when training with limited A tiny, friendly, strong baseline code for Object-reID (based on pytorch) since 2017. Most existing paradigms focus on visible human body parts through some external models to reduce noise interference. Recent image-based tasks mainly focus on person re-ID and vehicle re-ID. More precisely, the training of the Re-ID model is done assuming that we have access to the PersonViT: Large-scale Self-supervised Vision Transformer for Person Re-Identification Resources. (2021) in several ways but primarily because we aggregate the globally enhanced local tokens using a PCB-like strategy To overcome the problems of pose variation, complex background and more occlusion in video person re-identification, a network model ResTNet based on convolutional neural network and Transformer was proposed. In order to explore its ability in ReID task, Transformer-based person re-identification (person ReID) technologies tend to capture global information, but only focus on global features and ignore the interference of irrelevant information. [108] developed transformer architecture, two-fold loss, and context-specific modification to solve lighting, stance In this paper, we propose a novel network MDCTNet to mine diverse clues for person re-identification with transformers. Recently, Transformer-based networks, which can aggregate features of all the image patches to construct global features adaptively, have shown advantages in occluded person ReID. Visible-infrared person re-identification (VI-ReID) is a challenging task due to the large cross-modality discrepancies Keywords: Person Re-identification, Vision Transformer, Partial ReID 1. In this paper, we propose Person Re-Identification is an important problem in computer vision-based surveillance applications, in which the same person is attempted to be identified from surveillance photographs in a variety of nearby zones. [24] proposed a novel end-to-end Part Aware Transformer (PAT), which developed the encoder-decoder architecture of the transformer for occluded ReID. Most person re-identification methods focus on global and local features corresponding to the former two discriminations, cropping person images into horizontal strips to obtain coarse locations of body parts. Python 98. [44] and included a sub-network to predict the affine transformation utilized to Attribute-image person re-identification (AIPR) is a meaningful and challenging task to retrieve images based on attribute descriptions. It has also been applied to person Re-ID in recent years, mostly under the supervised setting. In this paper, we proposed a novel self-supervision and supervision combining transformer-based person re-identification framework, namely SSSC-TransReID. 3 watching. Re-Identification Transformer L. 2. We propose three stages of multi-granularity feature extraction, Person re-identification (re-ID) continues to pose a significant challenge, particularly in scenarios involving occlusions. The goal is to associate the same person across Cloth-changing person re-identification (CC Re-ID) is a challenging problem for the prevalence of clothing texture information which occupies most of the pixels in the image, becoming invalid information or even misleading information. In this work, we take advantage of CNNs and transformers, and propose a novel learning framework named convolutional multi-level transformer (CMT) for image-based Video-based person Re-identification (Re-ID) has received increasing attention re-cently due to its important role within surveillance video analysis. Breckon1 1 Department of Computer Science Durham University, UK 2 Department of Computer Science boost person re-identification whilst additionally capturing detailed long range feature de-pendencies. However, existing studies mostly use Transformers for feature representation learning, e. We address long-term ReID Extracting discriminative features using vision transformer is a popular research direction for person re-identification. It is crucial for ReID to extract extensive discriminative feature representations from images for achieving desirable performance. Video re-ID methods generally adopt frame-level feature extraction for different video frames, but they still lack effective spatio-temporal interaction, easily leading to the multi-frame misalignment problem. The transformer-based methods have achieved a comprehensive lead in accuracy since 2021, while Text-based person re-identification aims to find the target person from a large pedestrian gallery with the given natural language description. Different from most Person re-identification (ReID) is a type of image retrieval task that aims to find samples with the same ID as a given query image from a gallery set. The loss function is designed to account for both view-related and view-unrelated Recently, part information of pedestrian images has been demonstrated to be effective for person re-identification (ReID), but the part interaction is ignored when using Transformer to learn long-range dependencies. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, due to the high intra-identity variations, ignoring Plenty of efforts (He et al. Person re-identification aims to retrieve persons in highly varying settings across different cameras and scenarios, in which robust and discriminative representation learning is crucial. [13] introduce a pure transformer-based object Re-ID framework, showing state-of-the-art performance. AbstractPerson re-identification (ReID) is a computer vision-based autonomous process that re-identifies a query person from a set of gallery images captured by multiple non-overlapping cameras of a surveillance network. DPEFormer has the primary goal of localizing and selecting discriminative human body parts solely based on identity labels, without relying on Video-based person re-identification (re-ID) aims to match the same pedestrian of video sequences across non-overlapping cameras. g. It has drawn massive attention from both academia and industry due to its importance in various applications, such as video surveillance [ 1 ], activity analysis [ 2 ], and autonomous driving [ 3 ]. However, Transformer-based ReID models inevitably over-fit to domain-specific biases due to the Person Re-Identification (ReID) aims to retrieve relevant individuals in non-overlapping camera images and has a wide range of applications in the field of public safety. Specifically, we Extracting robust feature representation is one of the key challenges in object re-identification (ReID). In past decade, with the fast development of deep learning technology, person ReID has made great progress and been dominated by the deep learning Transformers have recently gained increasing attention in computer vision. However, CNN has the problems of information loss caused by down-sampling algorithm and remote dependence modeling. With the rapid development of convolution neural networks (CNNs) Most previous visible–infrared person re-identification methods emphasized learning modality-shared features to narrow the modality differences, while neglecting the benefits of modality-specific features for feature embedding and narrowing the modality gap. Existing AReID methods have provided limited solutions for the former, while the latter remains largely Person re-identification (ReID) is a crucial component of computer vision that has garnered increased attention in recent years, primarily due to its importance in applications such as video surveillance. To address this issue, we propose a Temporal Correlation Vision Transformer (TCViT) for video person Re-ID. However, the identifiable personal belongings like knapsack and reticule, which are crucial for person re-ID, cannot be recognized Abstract - Cloth-changing person re-identification is a subject closer to the real world, which focuses on solving the problem of person re-identification after pedestrians change clothes. The current person ReID research focuses on creating robust features for class distinction and generalizing neural networks for covering various target To determine whether a pedestrian of interest has been captured by another distinct camera across a network of non-overlapping cameras, or by the same camera at a distinct time, is known as the problem of person re-identification and is considered one of the most fascinating challenges in computer vision. Li et al. Weather conditions can have an impact on the re-identification task’s performance when performed outdoors. Stars. Wang and Q. In this article, we propose a novel transformer network named Completed Part Transformer (CPT) for person ReID, where we design the part For person Re-ID, He et al. Inspired by them, this work investigates the way of extracting multi-grained features from a pure transformer network to address the unsupervised Re-ID problem that is label-free but much more Currently, most existing person re-identification methods use instance-level features, which are extracted only from a single image. In this article, we introduce an Person re-identification (ReID) plays a significant role in intelligent surveillance systems. For instance, Bedagkar-Gala and Shah (2014) delves into the challenges of person Re-ID, categorizing it into open-set Re-ID and Person re-identification (ReID) typically encounters varying degrees of occlusion in real-world scenarios. Wang, J. Although convolution neural network (CNN)-based methods have achieved great success, they only process one local neighborhood at a time and suffer from information loss on details caused by convolution and downsampling operators (e. Transformer-based Person Re-ID Transformer has demonstrated its great potential in var-ious vision tasks [9,26]. To address these issues, this paper proposes Person re-identification (re-ID) aims to associate the specific person across cameras. The available reviews primarily focus on specific aspects of the field, and these surveys are summarized in Table 1. To explore the potential interactions among images and learn more robust representations, this paper proposes Transformer-based Feature Interactor(TFI) and improved Margin Self-punishment Person Re-Identification (ReID) aims to retrieve relevant individuals in non-overlapping camera images and has a wide range of applications in the field of public safety. With the rapid development of computing and storage capacity of edge sensors, performing person Re-Idon edge devices has become more and more popular in recent years. The stud-ies of person re-ID have paid attention to feature represen- Transformers for Person Re-identification Aishah Alsehaim1,2 Toby P. 1 Generalizable Person ReID. In order to solve this problem, we propose a Local Feature-Emphasizing Transformer for CC Re-ID (LFET Person re-identification aims to retrieve specific pedestrians from different cameras and scenes, in which extracting robust and discriminative features is crucial for this task. However, prior The goal of person re-identification (ReID) is to match pedestrians with the same identity in other camera views [3]. In recent years, with the development of Vision Transformer (ViT) and self-supervised learning techniques, the performance of person ReID based on self-supervised pre-training has been Person re-identification (ReID) as one subtask of the person tracking aims to identify the interested person from a large-scale gallery dataset captured by multiple non-overlapping cameras, making noticeable improvements, especially on video surveillance and criminal investigation [1]. Recently, transformer-based methods have emerged in the field of occluded person re-identification. 5% Rank-1 score and 53. Within the domain of person re-identification (ReID), partial ReID methods are considered mainstream, aiming to measure feature distances through comparisons of body parts between samples. (2) We designed the enhanced embedding module. In this work, we further investigate the possibility of applying Transformers for image matching and metric learning given pairs of images. To solve this problem, we design a vision transformer with multiple granularities for person re-identification. Although Person re-identification (ReID) is a type of image retrieval task that aims to find samples with the same ID as a given query image from a gallery set. Conventional pedestrian recognition models typically employ convolutional neural network (CNN) and vision transformer (ViT) networks to extract features, and while CNNs are adept at extracting local features through convolution of-the-art on three person re-ID benchmarks including MSMT17, Market-1501 and CUHK03. In order to train the contrastive learning branch, we also proposed a novel random The development of person re-identification (Re-ID) [2, 37] research has been driven by the need to overcome the challenges posed by multi-camera surveillance systems, where each camera possesses its distinctive viewpoint and coverage. In this way, the multi-head attention within a transformer architecture Due to some complex factors (e. This paper applies the Transformer to video-based person re-identification, where the key issue is to extract the discriminative information from a tracklet. ReID can effectively operate in various conditions, making it suitable for security, retail analytics, and smart city applications. 6% mAP score when applying Transformer encoder–decoder in the depth model for the first time. Transformer structures have been successfully applied in the field of VI Re-ID. However, discriminative clues The repository for [DC-Former: Diverse and Compact Transformer for Person Re-Identification] achieves state-of-the-art performances on 3 commonly used person re-ID including MSMT17, Market-1501 and CUHK03. Custom properties. In this Different from the general transformer-based person re-identification models, we designed a self-supervised contrastive learning branch, which can enhance the feature representation for person re-identification without negative samples or additional pre-training. When query image of person of interest gets In person re-identification (re-ID), extracting part-level features from person images has been verified to be crucial to offer fine-grained information. Strong. The key challenge in video person Re-ID lies in effectively constructing discriminative and robust person feature representations. No packages published . SWIN Transformer based Re-Identification network to generate embeddings for identifying persons in different scenes. Transformer architecture has achieved good performance in various visual tasks recently. 3. The framework incorporates N 𝑁 N italic_N Token Selection view-decoupled transformer (VDT) blocks, where each block consists of an encoder layer and a visual token selector. However, in real scenes, people are often In Fig. Person Re-ID is essential in an agile monitoring scheme having major scientific gist and functional significance, because of the pressing requirement for people’s safety and the quantity of tracking equipment. The major weakness of conventional convolution neural network (CNN) based methods is that they cannot extract long-range information from diverse parts, which can be alleviated by recently developed Transformers. In the Transformer ZHU et al. In CNN, since the receptive field of the latter layer is obtained by convolution operation on the feature map of the Humans always identify persons through their characteristics, salient attributes, and these attributes’ locations on the body. 18 stars. On the one hand, increasing more instances for each identity helps to recognize one person under different circumstances, extracting the most common and As person image variations are likely to cause a part misalignment problem, most previous person Re-Identification (ReID) works may adopt local feature partition or additional landmark annotations to acquire aligned person features and boost ReID performance. To "Video-based person Re-identification (Re-ID) has received increasing attention recently due to its important role within surveillance video analysis. However, existing research predominantly focuses on ReID performance in ideal dataset environments, often overlooking the challenges associated with implementing ReID Person re-identification (re-id) is an autonomous process that uses raw surveillance images to identify a person across multiple non-overlapping camera views without requiring any kind of hard biometrics like fingerprints, retina patterns or the facial images. However, the feature misalignment problem caused by discarded occlusions negatively affects the performance of the network. In this paper, we study the unsupervised person re-ID which aims to identify target identity across multiple non-overlapping cameras for intelligent surveillance systems. Prior approaches aimed at tackling occlusions have predominantly focused on aligning physical body features through the utilization of external semantic cues. The VVI-ReID task requires considering both the spatial relationship between body parts within each frame and the temporal change of appearance between successive frames. 12084}, year={2021} } Contact. Tian, S. In this work, we further investigate the possibility of applying Transformers for image matching Person re-identification (ReID) is designed to solve the problem of matching images of people captured by non-overlapping cameras. Person re-identification is a challenging task due to different viewpoints, low resolutions, illumination changes, unconstrained poses, occlusions, etc. , Joint Discriminative and Generative Learning for Person Re-identification(CVPR19), Beyond Part Models: Person Retrieval with Refined Part Pooling(ECCV18), Camera Style Adaptation for Person Re This work proposes a novel Cross-Modality Trans-former (CMT) to jointly explore a modality-level alignment module and an instance-level module for VI-ReID, the first work to exploit a cross-modality transformer to achieve the modality compensation for VI-ReID. When converting the feature map into a feature vector, a large number of convolution operations are used to reduce the size of the feature map. Since long, the convolution neural Recently, with the advance of deep Convolutional Neural Networks (CNNs), person Re-Identification (Re-ID) has witnessed great success in various applications. Although unsupervised person re-identification (Re-ID) has drawn increasing research attention, it still faces the challenge of learning discriminative features in the absence of pairwise labels across disjoint camera views. During training, data are The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. In this article, we propose a novel transformer network named Completed Part Transformer (CPT) for person ReID, where we design the part transformer layer to learn Person Re-identification, Transformer, Deep Feature Aggregation 1 INTRODUCTION Person Re-identification (Re-ID) aims to retrieve the same person under different cameras, places and times. Packages 0. obrd vjjm pbv rvfzfcpq lnltse zjswr czjgamqs yzhebb ztciw bwxvs