Person Re-identification Task Research Articles

CLIP-DFGS: A Hard Sample Mining Method for CLIP in Generalizable Person Re-Identification

Recent advancements in pre-trained vision-language models like CLIP have shown promise in person re-identification (ReID) applications. However, their performance in generalizable person re-identification tasks remains suboptimal. The large-scale and diverse image-text pairs used in CLIP's pre-training may lead to a lack or insufficiency of certain fine-grained features. In light of these challenges, we propose a hard sample mining method called DFGS (Depth-First Graph Sampler), based on depth-first search, designed to offer sufficiently challenging samples to enhance CLIP's ability to extract fine-grained features. DFGS can be applied to both the image encoder and the text encoder in CLIP. By leveraging the powerful cross-modal learning capabilities of CLIP, we aim to apply our DFGS method to extract challenging samples and form mini-batches with high discriminative difficulty, providing the image model with more efficient and challenging samples that are difficult to distinguish, thereby enhancing the model's ability to differentiate between individuals. Our results demonstrate significant improvements over other methods, confirming the effectiveness of DFGS in providing challenging samples that enhance CLIP's performance in generalizable person re-identification.

ADT: Person re-identification based on efficient attention mechanism and single-channel dual-channel fusion with transformer features aggregation

In person re-identification(Re-ID) tasks, using only convolutional neural network(CNNs) makes it challenging to fully exploit the global key information in person images, while using only the Transformer structure fails to adequately capture the multi-scale local detail information in person images. Therefore, to address the more challenging person Re-ID tasks, effectively integrating CNN and Transformer into an algorithmic framework capable of fusing multi-dimensional features has become one of the current focal points of research. Due to the ability of the attention mechanism to suppress the extraction of non-salient features by convolutional neural networks, and the capability of the dual-channel multi-scale mechanism to facilitate the extraction of finer-grained deep information by the Transformer, we propose a network Based on Efficient Attention Mechanism and Single-channel Dual-channel Fusion with Transformer Features Aggregation (ADT). First, we introduce the Convolutional Block Attention Module(CBAM) and Spatial Reconstruction Unit (SRU) into the CNN network to construct a Multi-dimensional Attention Features Re-fusion Network(MCF). By weighting the local features extracted in the convolutional network, attention to person detail features in the model is enhanced. Then, we propose the Single and Dual-channel Integrated Transformer Features Aggregator(SDT) and overlap it in MCF. This integration is designed to more effectively aggregate the multi-scale high-dimensional global features from each layer of the CNN network. Experimental results on the Market1501, DukeMTMC, MSMT17, and CUHK03 datasets demonstrate that our model significantly enriches features representation compared to most methods.

MSCMNet: Multi-scale Semantic Correlation Mining for Visible-Infrared Person Re-Identification

The main challenge in the Visible-Infrared Person Re-Identification (VI-ReID) task lies in extracting discriminative features from different modalities for matching purposes. While existing studies primarily focus on reducing modal discrepancies, the modality information fails to be thoroughly exploited. To solve this problem, the Multi-scale Semantic Correlation Mining network (MSCMNet) is proposed to comprehensively exploit semantic features at multiple scales. The network fuses shallow-level features into the deep network through dimensionality reduction and mapping, and the fused features are utilized to minimize modality information loss in feature extraction. Firstly, considering the effective utilization of modality information, the Multi-scale Information Correlation Mining Block (MIMB) is designed to fuse features at different scales and explore the semantic correlation of fusion features. Secondly, in order to enrich the semantic information that MIMB can utilize, the Quadruple-stream Feature Extractor (QFE) with non-shared parameters is specifically designed to extract information from different dimensions of the dataset. Finally, the Quadruple Center Triplet Loss (QCT) is further proposed to address the information discrepancy in the comprehensive features. Extensive experiments on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that the proposed MSCMNet achieves the greatest accuracy. We release the source code on https://github.com/Hua-XC/MSCMNet.

Instant pose extraction based on mask transformer for occluded person re-identification

Re-Identification (Re-ID) of obscured pedestrians is a daunting task, primarily due to the frequent occlusion caused by various obstacles like buildings, vehicles, and even other pedestrians. To address this challenge, we propose a novel approach named Instant Pose Extraction based on Mask Transformer (MTIPE), tailored specifically for occluded person Re-ID. MTIPE consists of several new modules: a Mask Aware Module (MAM) for alignment between the overall prototype and the occluded image; a Multi-headed Attention Constraint Module (MACM) to enrich the feature representation; a Pose Aggregation Module (PAM) to separate useful human information from the occlusion noise; a Feature Matching Module (FMM) in matching non-occluded parts; introduction of learnable local prototypes in the defined local prototype-based transformer decoder; a Pooling Attention Module (PAM) instead of traditional self-attention module to better extract and propagate local contextual information; and Pose Key-points Loss to better match non-occluded body parts. Through comprehensive experimental evaluations and comparisons, MTIPE demonstrates encouraging performance improvements in both occluded and holistic person Re-ID tasks. Its results surpass or at least match those of current state-of-the-art methods in various aspects, highlighting its potential advantages and promising application prospects.

A Multi-Scale Graph Attention-Based Transformer for Occluded Person Re-Identification

The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human key point detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of key points. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model’s robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to reweight the importance of features is proposed in this study, significantly enhancing the model’s robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded datasets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores.

A review on video person re-identification based on deep learning

Person Re-Identification (ReID) is an essential technology for matching a person across non-overlapping cameras. It has attracted increasing attention in recent years due to its wide range of applications in various real-world scenarios such as security surveillance and criminal investigation. Different from other person ReID tasks, video-based ReID uses a video clip as the retrieval input, which can provide more promising ReID performance because that the video has rich information on appearance, motion cues and pose variations on temporal pipeline. Over the last few years, many deep learning-based video person ReID have been proposed to address various challenges, such as illumination variation, complex background, occlusion, etc. To provide a more comprehensive and readable review on existing video-based person ReID methods, we propose a novel taxonomy method that observes existing methods from four perspectives: data, algorithms, computing power, and applications. Specifically, we first introduce some popular datasets and evaluation criterion used for video-based person ReID. Next, from limited data and little annotation view, we introduce data augmentation and unsupervised learning ReID. From algorithm view, we focus on reviewing supervised methods including spatial feature learning, temporal feature learning and spatio-temporal feature learning, and further discuss and conduct a systematic comparison among these approaches. From complex open-world application view, we mainly summarized domain adaption and multimodal ReID. From insufficient GPU computing power view, we mainly discuss modality-agnostic unified large-scale ReID and their lightweighting. Finally, we provide a discussion of open problems and potential research directions for the community.

FRCE: Transformer-based feature reconstruction and cross-enhancement for occluded person re-identification

Occluded person re-identification (ReID) aims to capture the same person from different camera views with partial occlusion. In real-world scenarios, any object can become a random occluder, which is still an unsettled challenge for person ReID. Previous methods used auxiliary models to locate or reconstruct the occluded parts for ReID, but these methods are affected by external models or unable to model well, leaving the difficulty unresolved. Currently, Transformer has been widely applied to occluded person ReID tasks, but due to the lack of attention to local features, these methods still have limitations. Therefore, we propose a Feature Reconstruction and Cross-Enhancement (FRCE) network. Firstly, a Cross-level Feature Reconstruction Distiller (CFRD) is designed to improve the feature richness in the recognition scope and generate higher quality identity feature mapping by reconstructing the effective recognition region of the target. Secondly, we introduce a Multi-Dimensional Local Refinement (MDLR) module to deeply excavate local details in spatial and channel dimensions, seek subtle target cues and similarity relationships at local angles, and achieve more efficient ReID. Extensive experiments on five challenging occluded and holistic person ReID datasets demonstrate the effectiveness of our approach.

PSF-C-Net: A Counterfactual Deep Learning Model for Person Re-Identification Based on Random Cropping Patch and Shuffling Filling

In the task of person re-identification (re-ID), capturing the long-range dependency of instances is crucial for accurate identification. The existing methods excel at extracting local features but often overlook the global information of instance images. To address this limitation, we propose a convolution-based counterfactual learning framework, called PSF-C-Net, to focus on global information rather than local detailed features. PSF-C-Net adopts a parameter-sharing dual-path structure to perform counterfactual operations in the prediction space. It takes both the actual instance image and a counterfactual instance image that disrupts the contextual relationship as the input. The counterfactual framework enables the interpretable modeling of global features without introducing additional parameters. Additionally, we propose a novel method for generating counterfactual instance images, which effectively constructs an explicit counterfactual space, to reliably implement counterfactual strategies. We have conducted extensive experiments to evaluate the performance of PSF-C-Net on the Market-1501 and Duke-MTMC-reID datasets. The results demonstrate that PSF-C-Net achieves state-of-the-art performance.

Situational diversity in video person re-identification: introducing MSA-BUPT dataset

Thanks to the success of deep learning over the past few years, the video person re-identification (ReID) algorithms have achieved high accuracy on multiple public benchmark datasets. However, the available video person ReID datasets cover a limited range of real-world scenarios, and they have several obvious limitations: limited camera viewing angles, tiny variations of the shooting scene, and even errors in manual labels. These disadvantages prevent video person ReID from being widely used in real-life scenarios. In this work, a new high-quality multi-situation video person ReID dataset, named MSA-BUPT, is built to promote the video person ReID task in large-scale urban surveillance. Specifically, MSA-BUPT contains 684 identities, 2,665 trajectories, and nearly 250,000 frames from 200-h videos across various complex scenarios. Person attribute annotations and unannotated video data are also provided for other research perspectives, such as cross-modality ReID, cross-domain ReID, and so on. Furthermore, two plug-and-play components are used to improve retrieval capabilities: a new scenario-based data augmentation method is proposed to alleviate the person misalignment problem; a re-ranking strategy based on person attribute is applied to make secondary adjustments to the content to the results of the model. The extensive experimental results show that the above methods improve the performance of some representative state-of-the-art models on the new dataset.

IGIE-net: Cross-modality person re-identification via intermediate modality image generation and discriminative information enhancement

Given an RGB image (or an IR image) of a pedestrian, the task of cross-modality person re-identification aims to retrieve images of a specific pedestrian from an IR (or RGB) gallery. However, the huge modality discrepancy between RGB and IR images significantly affects the performance of cross-modality person re-identification. For the purpose of reducing the impact of modality discrepancy and extracting more discriminative pedestrian features, a new cross-modality person re-identification network is proposed by us, which is based on intermediate modality image generation and discriminative information enhancement (IGIE-Net). Specifically, we design an intermediate modality image generation module (IMIGM) to effectively weaken the impact of modality discrepancy, which first extracts the modality-specific and modality-shared information contained in RGB and IR images separately, and then generates intermediate RGB and intermediate IR images by adaptively fusing original RGB and original IR images with their corresponding modality-specific and modality-shared information separately. In addition, we also design a discriminative information enhancement module (DIEM) to obtain more discriminative pedestrian representations by enhancing the discriminative information contained in the deep features of pedestrians. Extensive experiments on the publicly available SYSU-MM01 and RegDB datasets indicate that the performance of IGIE-Net reaches the current advanced level.

Instance-level Adversarial Source-free Domain Adaptive Person Re-identification

Domain adaption (DA) for person re-identification (ReID) has attained considerable progress by transferring knowledge from a source domain with labels to a target domain without labels. Nonetheless, most of the existing methods require access to source data, which raises privacy concerns. Source-free DA has recently emerged as a response to these privacy challenges, yet its direct application to open-set pedestrian re-identification tasks is hindered by the reliance on a shared category space in existing methods. Current source-free DA approaches for person ReID still encounter several obstacles, particularly the divergence-agnostic problem and the notable domain divergence due to the absent source data. In this article, we introduce an Instance-level Adversarial Mutual Teaching (IAMT) framework, which utilizes adversarial views to tackle the challenges mentioned above. Technically, we first elaborately develop a variance-based division (VBD) module to segregate the target data into instance-level subsets based on their similarity and dissimilarity to the source using the source-trained model, implicitly tackling the divergence-agnostic problem. To mitigate domain divergence, we additionally introduce a dynamic adversarial alignment (DAA) strategy, aiming to enhance the consistence of feature distribution across domains by employing adversarial instances from the target data to confuse the discriminators. Experiments reveal the superiority of the IAMT over state-of-the-art methods for DA person ReID tasks, while preserving the privacy of the source data.

Multi-Prompts Learning with Cross-Modal Alignment for Attribute-Based Person Re-identification

The fine-grained attribute descriptions can significantly supplement the valuable semantic information for person image, which is vital to the success of person re-identification (ReID) task. However, current ReID algorithms typically failed to effectively leverage the rich contextual information available, primarily due to their reliance on simplistic and coarse utilization of image attributes. Recent advances in artificial intelligence generated content have made it possible to automatically generate plentiful fine-grained attribute descriptions and make full use of them. Thereby, this paper explores the potential of using the generated multiple person attributes as prompts in ReID tasks with off-the-shelf (large) models for more accurate retrieval results. To this end, we present a new framework called Multi-Prompts ReID (MP-ReID), based on prompt learning and language models, to fully dip fine attributes to assist ReID task. Specifically, MP-ReID first learns to hallucinate diverse, informative, and promptable sentences for describing the query images. This procedure includes (i) explicit prompts of which attributes a person has and furthermore (ii) implicit learnable prompts for adjusting/conditioning the criteria used towards this person identity matching. Explicit prompts are obtained by ensembling generation models, such as ChatGPT and VQA models. Moreover, an alignment module is designed to fuse multi-prompts (i.e., explicit and implicit ones) progressively and mitigate the cross-modal gap. Extensive experiments on the existing attribute-involved ReID datasets, namely, Market1501 and DukeMTMC-reID, demonstrate the effectiveness and rationality of the proposed MP-ReID solution.

Enhancing long-term person re-identification using global, local body part, and head streams

This work addresses the task of long-term person re-identification. Typically, person re-identification assumes that people do not change their clothes, which limits its applications to short-term scenarios. To overcome this limitation, we investigate long-term person re-identification, which considers both clothes-changing and clothes-consistent scenarios. In this paper, we propose a novel framework that effectively learns and utilizes both global and local information. The proposed framework consists of three streams: global, local body part, and head streams. The global and head streams encode identity-relevant information from an entire image and a cropped image of the head region, respectively. Both streams encode the most distinct, less distinct, and average features using the combinations of adversarial erasing, max pooling, and average pooling. The local body part stream extracts identity-related information for each body part, allowing it to be compared with the same body part from another image. Since body part annotations are not available in re-identification datasets, pseudo-labels are generated using clustering. These labels are then utilized to train a body part segmentation head in the local body part stream. The proposed framework is trained by backpropagating the weighted summation of the identity classification loss, the pair-based loss, and the pseudo body part segmentation loss. To demonstrate the effectiveness of the proposed method, we conducted experiments on three publicly available datasets (Celeb-reID, PRCC, and VC-Clothes). The experimental results demonstrate that the proposed method outperforms the previous state-of-the-art method.

Focus and imagine: Occlusion suppression and repairing transformer for occluded person re-identification

Occlusion scenarios pose a great challenge to person re-identification (ReID) task because various occlusions may weaken the discriminative features and introduce interference. 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. Existing methods mainly adopted Transformer as a feature extractor and enhanced local features from the output of the Transformer encoder. However, during the processing of self-attention blocks, disturbing features from occlusions may be diffused into all the tokens, making it difficult to construct effective local features. Therefore, we consider predicting the occlusion situation of images before feature extraction and guiding the Transformer encoder to focus on visible regions, suppressing interference from occlusion. Furthermore, we propose to imagine the partial target under occlusion and reconstruct pseudo-holistic features for more robust retrieval. To this end, the Occlusion Suppression and Repairing Transformer (OSRTrans) is proposed. First, we use a self-supervised occlusion predictor to predict occlusion scores of image patches. Then the Occlusion Suppression Encoder (OSE), guided by occlusion predictions, suppresses the interference from occlusion regions and constructs a global feature. Finally, inspired by contrastive learning, the Feature Repairing Head (FRH) is proposed to reconstruct pseudo-holistic features. Our method enhances model’s ability of extracting discriminative local features, and achieve the state-of-the-art performance on occluded person ReID benchmarks, e.g., Rank-1 of 72.9% on Occluded-DukeMTMC.

Multiple-local feature and attention fused person re-identification method

Person re-identification (ReID) is widely used in intelligent security, monitoring, criminal investigation and other fields. Aiming at the problems of local occlusion, scale misalignment and attitude change of pedestrian images in actual scenes, we propose a Multi-local Feature and Attention fused network (MFA) used for person re-identification task. Firstly, Channel Point Affinity Attention module (CPAA) is embedded in the backbone network to enhance the ability of the network for extracting local details. The feature map output from the backbone network is horizontally segmented into four local feature maps, and further four branch networks are concatenated to the feature map of the backbone network. The four local feature maps are used to guide the four branch networks to pay more attention on different areas of pedestrians through Global Local Aligned loss (GLA) function. Finally, the pedestrian feature vector containing multi-local features is obtained. The mAP of the network on Market-1501, DukeMTMC-reID,CUHK03 and MSMT17 datasets were 88.6%, 81.4%, 79.5% and 64.7%, and the Rank-1 was 95.8%, 90.1%, 81.2% and 84.1% respectively. In addition, the model also obtained 73.2% and 68.1% of Rank-1 on partial dataset Patial-REID and Patial-iLIDS, respectively. Recently, The MFA model parameter is 28.3M and the inference efficiency is approximately 32 fps to an image with a resulation of 256 × 128. Compared with other ReID methods, our proposed methods achieved a competitive performance for ReID task. The code was available at github:git@github.com:ISCLab-Bistu/MFA.git.

Enhanced visible–infrared person re-identification based on cross-attention multiscale residual vision transformer

Visible–infrared (VI) person re-identification (Re-ID) is a critical identification task that involves retrieving and matching images of an individual using both infrared and visible imaging modalities. To improve the performance, researchers have developed methods to obtain implicit feature information; however, this degrades with fewer discriminative features. To address this issue, we propose a weighted fused cross-attention multi-scale residual vision transformer (WF-CAMReViT) approach to re-identify the appropriate person from visible–infrared modality images by integrating the cross-attention multi-scale residual vision transformer architecture with Opposition-based Dove Swarm Optimization (ODSO). The proposed framework aims to bridge the domain gap between the visible and infrared modalities and significantly improve the re-identification performance. RGB (visible) and infrared (IR) images of persons are gathered from standard datasets, subjected to a cross-attention multi-scale residual vision transformer network to collect features, and then fuse using minimal weight. We also propose Opposition-based DSO to find the minimal weight. The weighted fused features are then subjected to the final decoder layer of CAMReViT to perceive the characteristics of each modality. In this study, model-aware enhancement (MAE) loss is develop to improve the modality information capacity of modality-shared features. Then, the experimental results on the SYSU-MM01 and RegDB datasets are compared with state-of-the-art transformer-based visible–infrared person Re-ID tasks to verify the efficacy of the proposed model.

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.

Image-Level Adaptive Adversarial Ranking for Person Re-Identification.

The potential vulnerability of deep neural networks and the complexity of pedestrian images, greatly limits the application of person re-identification techniques in the field of smart security. Current attack methods often focus on generating carefully crafted adversarial samples or only disrupting the metric distances between targets and similar pedestrians. However, both aspects are crucial for evaluating the security of methods adapted for person re-identification tasks. For this reason, we propose an image-level adaptive adversarial ranking method that comprehensively considers two aspects to adapt to changes in pedestrians in the real world and effectively evaluate the robustness of models in adversarial environments. To generate more refined adversarial samples, our image representation enhancement module leverages channel-wise information entropy, assigning varying weights to different channels to produce images with richer information content, along with a generative adversarial network to create adversarial samples. Subsequently, for adaptive perturbation of ranking, the adaptive weight confusion ranking loss is presented to calculate the weights of distances between positive or negative samples and query samples. It endeavors to push positive samples away from query samples and bring negative samples closer, thereby interfering with the ranking of system. Notably, this method requires no additional hyperparameter tuning or extra data training, making it an adaptive attack strategy. Experimental results on large-scale datasets such as Market1501, CUHK03, and DukeMTMC demonstrate the effectiveness of our method in attacking ReID systems.

Address the Unseen Relationships: Attribute Correlations in Text Attribute Person Search.

Text attribute person search aims to identify the particular pedestrian by textual attribute information. Compared to person re-identification tasks which requires imagery samples as its query, text attribute person search is more useful under the circumstance where only witness is available. Most existing text attribute person search methods focus on improving the matching correlation and alignments by learning better representations of person-attribute instance pairs, with few consideration of the latent correlations between attributes. In this work, we propose a graph convolutional network (GCN) and pseudo-label-based text attribute person search method. Concretely, the model directly constructs the attribute correlations by label co-occurrence probability, in which the nodes are represented by attribute embedding and edges are by the filtered correlation matrix of attribute labels. In order to obtain better representations, we combine the cross-attention module (CAM) and the GCN. Furthermore, to address the unseen attribute relationships, we update the edge information through the instances through testing set with high predicted probability thus to better adapt the attribute distribution. Extensive experiments illustrate that our model outperforms the existing state-of-the-art methods on publicly available person search benchmarks: Market-1501 and PETA.

A Generative-Based Image Fusion Strategy for Visible-Infrared Person Re-Identification

Cross-modality person re-identification task is a challenging task aiming to recognize images of the same identity between different modalities. To alleviate the cross-modality discrepancies between images, existing approaches mainly guide models to mine modality invariant features. Although those approaches are effective, they lose the modality-specific features that include important information beneficial to VI-ReID. Therefore, some approaches are using generative adversarial networks to compensate for modality information. However, the quality of images generated by these methods is usually poor, and most of them focus only on the learning of modality-sharable features. To solve these problems, this paper proposes a generative-based cross-modality image fusion strategy (GC-IFS), which can generate high-quality cross-modality paired images and fuse the information of the two modalities. Firstly, considering the importance of the identity discriminative information of the generated image, we propose a contrastive-learning image generation (CLIG) network to generate cross-modality paired images. Meanwhile, to fully integrate and utilize the information of the two modalities and eliminate the influence of cross-modality discrepancies, we design a part-based dual multi-modality feature fusion (P-DMFF) module to extract the unified feature representation. Extensive experiments on SYSU-MM01 and RegDB datasets demonstrate that our strategy outperforms the state-of-the-art methods for the VI-ReID task.