Person_Re-identification
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Self-Reinforcing Prototype Evolution with Dual-Knowledge Cooperation for Semi-Supervised Lifelong Person Re-Identification
2025-07-24 02:26:33Kunlun Xu, Fan Zhuo, Jiangmeng Li, Xu Zou, Jiahuan ZhouAbstract
Current lifelong person re-identification (LReID) methods predominantly rely on fully labeled data streams. However, in real-world scenarios where annotation resources are limited, a vast amount of unlabeled data coexists with scarce labeled samples, leading to the Semi-Supervised LReID (Semi-LReID) problem where LReID methods suffer severe performance degradation. Existing LReID methods, even when combined with semi-supervised strategies, suffer from limited long-term adaptation performance due to struggling with the noisy knowledge occurring during unlabeled data utilization. In this paper, we pioneer the investigation of Semi-LReID, introducing a novel Self-Reinforcing Prototype Evolution with Dual-Knowledge Cooperation framework (SPRED). Our key innovation lies in establishing a self-reinforcing cycle between dynamic prototype-guided pseudo-label generation and new-old knowledge collaborative purification to enhance the utilization of unlabeled data. Specifically, learnable identity prototypes are introduced to dynamically capture the identity distributions and generate high-quality pseudo-labels. Then, the dual-knowledge cooperation scheme integrates current model specialization and historical model generalization, refining noisy pseudo-labels. Through this cyclic design, reliable pseudo-labels are progressively mined to improve current-stage learning and ensure positive knowledge propagation over long-term learning. Experiments on the established Semi-LReID benchmarks show that our SPRED achieves state-of-the-art performance. Our source code is available at this https URL
Abstract (translated)
当前的人终身重识别（LReID）方法主要依赖于完全标注的数据流。然而，在实际场景中，由于注释资源有限，大量的未标记数据与稀少的已标记样本并存，导致了半监督人终身重识别（Semi-LReID）问题：在这种情况下，LReID 方法的表现会严重下降。现有的 LReID 方法即使结合了半监督策略，也因为无法妥善处理在使用未标注数据时产生的噪声知识而长期适应性表现有限。 本文首次研究了 Semi-LReID 问题，并提出了一种新颖的自强化原型进化与双知识合作框架（SPRED）。我们的关键创新在于建立了动态原型引导伪标签生成和新旧知识协同净化之间的自我增强循环，以提高未标注数据的利用效率。具体而言，我们引入可学习的身份原型来动态捕捉身份分布并生成高质量的伪标签。接着，双知识合作方案整合了当前模型的专业化能力和历史模型的泛化能力，从而精炼出噪声伪标签。通过这种循环设计，SPRED 能够逐步挖掘可靠伪标签以提升现阶段的学习效果，并确保长期学习中积极的知识传播。 在建立的 Semi-LReID 基准上进行的实验表明，我们的 SPRED 达到了最先进的性能水平。源代码可在提供的链接获取（假设该链接指向一个公开可用的位置）。
URL
https://arxiv.org/abs/2507.01884
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AG-VPReID.VIR: Bridging Aerial and Ground Platforms for Video-based Visible-Infrared Person Re-ID
2025-07-24 00:13:25Huy Nguyen, Kien Nguyen, Akila Pemasiri, Akmal Jahan, Clinton Fookes, Sridha SridharanAbstract
Person re-identification (Re-ID) across visible and infrared modalities is crucial for 24-hour surveillance systems, but existing datasets primarily focus on ground-level perspectives. While ground-based IR systems offer nighttime capabilities, they suffer from occlusions, limited coverage, and vulnerability to obstructions--problems that aerial perspectives uniquely solve. To address these limitations, we introduce this http URL, the first aerial-ground cross-modality video-based person Re-ID dataset. This dataset captures 1,837 identities across 4,861 tracklets (124,855 frames) using both UAV-mounted and fixed CCTV cameras in RGB and infrared modalities. this http URL presents unique challenges including cross-viewpoint variations, modality discrepancies, and temporal dynamics. Additionally, we propose TCC-VPReID, a novel three-stream architecture designed to address the joint challenges of cross-platform and cross-modality person Re-ID. Our approach bridges the domain gaps between aerial-ground perspectives and RGB-IR modalities, through style-robust feature learning, memory-based cross-view adaptation, and intermediary-guided temporal modeling. Experiments show that this http URL presents distinctive challenges compared to existing datasets, with our TCC-VPReID framework achieving significant performance gains across multiple evaluation protocols. Dataset and code are available at this https URL.
Abstract (translated)
跨可见光与红外模态的人员再识别（Person Re-ID）对于全天候监控系统至关重要，但现有数据集主要关注地面视角。虽然基于地面的IR系统提供了夜间工作能力，但由于遮挡、覆盖范围有限和易受障碍物影响等问题，它们表现不佳——这些问题可以通过空中视角来解决。为了解决这些局限性，我们引入了[此处应有一个链接]（请替换为实际的数据集链接），这是第一个跨模态视频基的空地人员再识别数据集。该数据集使用无人机搭载和固定CCTV摄像机在RGB和红外模式下记录了1,837个身份跨越4,861个轨迹片段（共124,855帧）。此数据集带来了独特的挑战，包括跨视角变化、模态差异以及时间动态性。此外，我们提出了一种新的三流架构TCC-VPReID，旨在解决跨平台和跨模式人员再识别的联合挑战。我们的方法通过风格稳健的功能学习、基于记忆的跨视图适应以及中介引导的时间建模来弥合空地视角之间及RGB-IR模态之间的领域差距。实验表明，[此处应有一个链接]（请替换为实际的数据集链接）与现有数据集相比具有独特的挑战性，并且我们的TCC-VPReID框架在多种评估协议中取得了显著的性能提升。该数据集和代码可在[此处应有一个链接]（请替换为实际的网址）获取。 请注意，您需要将方括号内的占位符文本替换成实际的数据集链接和相关网站地址。
URL
https://arxiv.org/abs/2507.17995
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Positive Style Accumulation: A Style Screening and Continuous Utilization Framework for Federated DG-ReID
2025-07-22 05:21:00Xin Xu (Wuhan University of Science and Technology), Chaoyue Ren (Wuhan University of Science and Technology), Wei Liu (Wuhan University of Science and Technology), Wenke Huang (Wuhan University), Bin Yang (Wuhan University), Zhixi Yu (Wuhan University of Science and Technology), Kui Jiang (Harbin Institute of Technology)Abstract
The Federated Domain Generalization for Person re-identification (FedDG-ReID) aims to learn a global server model that can be effectively generalized to source and target domains through distributed source domain data. Existing methods mainly improve the diversity of samples through style transformation, which to some extent enhances the generalization performance of the model. However, we discover that not all styles contribute to the generalization performance. Therefore, we define styles that are beneficial or harmful to the model's generalization performance as positive or negative styles. Based on this, new issues arise: How to effectively screen and continuously utilize the positive styles. To solve these problems, we propose a Style Screening and Continuous Utilization (SSCU) framework. Firstly, we design a Generalization Gain-guided Dynamic Style Memory (GGDSM) for each client model to screen and accumulate generated positive styles. Meanwhile, we propose a style memory recognition loss to fully leverage the positive styles memorized by Memory. Furthermore, we propose a Collaborative Style Training (CST) strategy to make full use of positive styles. Unlike traditional learning strategies, our approach leverages both newly generated styles and the accumulated positive styles stored in memory to train client models on two distinct branches. This training strategy is designed to effectively promote the rapid acquisition of new styles by the client models, and guarantees the continuous and thorough utilization of positive styles, which is highly beneficial for the model's generalization performance. Extensive experimental results demonstrate that our method outperforms existing methods in both the source domain and the target domain.
Abstract (translated)
联邦领域泛化人物再识别（FedDG-ReID）旨在通过分布式源域数据学习一个全局服务器模型，使其能够有效地推广到源域和目标域。现有方法主要通过风格转换来提高样本的多样性，在某种程度上增强了模型的泛化性能。然而，我们发现并非所有风格都对模型的泛化性能有贡献。因此，我们将有益于或有害于模型泛化性能的风格定义为正向风格或负向风格。基于此，新的问题出现了：如何有效地筛选和持续利用正向风格。为了应对这些问题，我们提出了一种风格筛选与连续利用（SSCU）框架。 首先，我们设计了针对每个客户端模型的泛化收益引导动态风格记忆（GGDSM），用于筛选并积累生成的正向风格。与此同时，我们提出了一个风格记忆识别损失，以充分利用内存中记住的正向风格。此外，我们还提出了一种协作式风格训练（CST）策略，以便充分使用正向风格。 与传统的学习策略不同，我们的方法利用了新产生的风格以及存储在内存中的积累的正向风格，在两个不同的分支上训练客户端模型。这种训练策略旨在有效促进客户端模型快速获取新的风格，并确保持续和彻底地利用正向风格，这对模型的泛化性能非常有利。广泛的实验结果表明，我们提出的方法在源域和目标域上均优于现有的方法。
URL
https://arxiv.org/abs/2507.16238
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When Person Re-Identification Meets Event Camera: A Benchmark Dataset and An Attribute-guided Re-Identification Framework
2025-07-18 05:04:59Xiao Wang, Qian Zhu, Shujuan Wu, Bo Jiang, Shiliang Zhang, Yaowei Wang, Yonghong Tian, Bin LuoAbstract
Recent researchers have proposed using event cameras for person re-identification (ReID) due to their promising performance and better balance in terms of privacy protection, event camera-based person ReID has attracted significant attention. Currently, mainstream event-based person ReID algorithms primarily focus on fusing visible light and event stream, as well as preserving privacy. Although significant progress has been made, these methods are typically trained and evaluated on small-scale or simulated event camera datasets, making it difficult to assess their real identification performance and generalization ability. To address the issue of data scarcity, this paper introduces a large-scale RGB-event based person ReID dataset, called EvReID. The dataset contains 118,988 image pairs and covers 1200 pedestrian identities, with data collected across multiple seasons, scenes, and lighting conditions. We also evaluate 15 state-of-the-art person ReID algorithms, laying a solid foundation for future research in terms of both data and benchmarking. Based on our newly constructed dataset, this paper further proposes a pedestrian attribute-guided contrastive learning framework to enhance feature learning for person re-identification, termed TriPro-ReID. This framework not only effectively explores the visual features from both RGB frames and event streams, but also fully utilizes pedestrian attributes as mid-level semantic features. Extensive experiments on the EvReID dataset and MARS datasets fully validated the effectiveness of our proposed RGB-Event person ReID framework. The benchmark dataset and source code will be released on this https URL
Abstract (translated)
近期的研究人员提出利用事件相机进行行人重识别（ReID），因为其表现出色且在隐私保护方面更加平衡，基于事件相机的行人ReID吸引了广泛的关注。目前主流的基于事件的行人ReID算法主要集中在融合可见光和事件流数据，以及保护隐私上。虽然已取得了显著进步，但这些方法通常是在小规模或模拟的事件相机数据集上进行训练和评估的，难以对其实际识别性能和泛化能力进行全面评估。 为了解决数据稀缺的问题，本文引入了一个大规模的RGB-事件行人ReID数据集，称为EvReID。该数据集包含118,988对图像和涵盖1200名行人的身份信息，数据采集跨越了多个季节、场景及光照条件。我们还评估了15种最先进的行人ReID算法，为未来的研究在数据与基准测试方面奠定了坚实基础。 基于新构建的数据集，本文进一步提出了一种以行人属性为导向的对比学习框架TriPro-ReID，旨在增强行人重识别中的特征学习能力。该框架不仅有效地探索了RGB帧和事件流中视觉特征，还充分利用了行人的属性作为中间层次语义特征。在EvReID数据集和MARS数据集上的广泛实验充分验证了我们所提出的RGB-事件行人ReID框架的有效性。 基准数据集及源代码可在以下链接获取：[https://this-url.com](https://this-url.com)（请将URL替换为实际发布地址）。
URL
https://arxiv.org/abs/2507.13659
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Weakly Supervised Visible-Infrared Person Re-Identification via Heterogeneous Expert Collaborative Consistency Learning
2025-07-17 09:31:34Yafei Zhang, Lingqi Kong, Huafeng Li, Jie WenAbstract
To reduce the reliance of visible-infrared person re-identification (ReID) models on labeled cross-modal samples, this paper explores a weakly supervised cross-modal person ReID method that uses only single-modal sample identity labels, addressing scenarios where cross-modal identity labels are unavailable. To mitigate the impact of missing cross-modal labels on model performance, we propose a heterogeneous expert collaborative consistency learning framework, designed to establish robust cross-modal identity correspondences in a weakly supervised manner. This framework leverages labeled data from each modality to independently train dedicated classification experts. To associate cross-modal samples, these classification experts act as heterogeneous predictors, predicting the identities of samples from the other modality. To improve prediction accuracy, we design a cross-modal relationship fusion mechanism that effectively integrates predictions from different experts. Under the implicit supervision provided by cross-modal identity correspondences, collaborative and consistent learning among the experts is encouraged, significantly enhancing the model's ability to extract modality-invariant features and improve cross-modal identity recognition. Experimental results on two challenging datasets validate the effectiveness of the proposed method.
Abstract (translated)
为了减少可见光-红外行人重识别（ReID）模型对标注的跨模态样本的依赖，本文探索了一种仅使用单一模态样本身份标签的弱监督跨模态行人ReID方法，以应对跨模态身份标签不可用的情况。为减轻缺少跨模态标签对模型性能的影响，我们提出了一种异构专家协作一致性学习框架，旨在通过一种弱监督方式建立稳健的跨模态身份对应关系。该框架利用每个模态上的标注数据独立训练专用分类专家。为了关联跨模态样本，这些分类专家作为异构预测器，预测来自另一模态样本的身份信息。为提高预测准确性，我们设计了一种跨模态关系融合机制，有效整合不同专家的预测结果。在隐式提供的跨模态身份对应监督下，鼓励专家之间进行协作和一致学习，显著增强了模型提取模态不变特征的能力，并提高了跨模态身份识别性能。实验结果显示，在两个具有挑战性的数据集上验证了所提出方法的有效性。
URL
https://arxiv.org/abs/2507.12942
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WhoFi: Deep Person Re-Identification via Wi-Fi Channel Signal Encoding
2025-07-17 07:40:50Danilo Avola, Daniele Pannone, Dario Montagnini, Emad EmamAbstract
Person Re-Identification is a key and challenging task in video surveillance. While traditional methods rely on visual data, issues like poor lighting, occlusion, and suboptimal angles often hinder performance. To address these challenges, we introduce WhoFi, a novel pipeline that utilizes Wi-Fi signals for person re-identification. Biometric features are extracted from Channel State Information (CSI) and processed through a modular Deep Neural Network (DNN) featuring a Transformer-based encoder. The network is trained using an in-batch negative loss function to learn robust and generalizable biometric signatures. Experiments on the NTU-Fi dataset show that our approach achieves competitive results compared to state-of-the-art methods, confirming its effectiveness in identifying individuals via Wi-Fi signals.
Abstract (translated)
人员重新识别是视频监控中的一个关键且具有挑战性的任务。传统方法依赖于视觉数据，但由于照明不良、遮挡和视角不佳等问题，其性能常常受到限制。为了解决这些挑战，我们提出了一种名为WhoFi的新颖管道系统，该系统利用Wi-Fi信号来进行人员重新识别。从信道状态信息（CSI）中提取生物特征，并通过一个模块化的深度神经网络进行处理，该网络采用基于Transformer的编码器。通过批内负样本损失函数对该网络进行训练，以学习稳健且通用性强的生物签名。 在NTU-Fi数据集上的实验表明，我们提出的方法与当前最先进的方法相比取得了具有竞争力的结果，这证实了利用Wi-Fi信号识别个体的有效性。
URL
https://arxiv.org/abs/2507.12869
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Try Harder: Hard Sample Generation and Learning for Clothes-Changing Person Re-ID
2025-07-15 09:14:01Hankun Liu, Yujian Zhao, Guanglin NiuAbstract
Hard samples pose a significant challenge in person re-identification (ReID) tasks, particularly in clothing-changing person Re-ID (CC-ReID). Their inherent ambiguity or similarity, coupled with the lack of explicit definitions, makes them a fundamental bottleneck. These issues not only limit the design of targeted learning strategies but also diminish the model's robustness under clothing or viewpoint changes. In this paper, we propose a novel multimodal-guided Hard Sample Generation and Learning (HSGL) framework, which is the first effort to unify textual and visual modalities to explicitly define, generate, and optimize hard samples within a unified paradigm. HSGL comprises two core components: (1) Dual-Granularity Hard Sample Generation (DGHSG), which leverages multimodal cues to synthesize semantically consistent samples, including both coarse- and fine-grained hard positives and negatives for effectively increasing the hardness and diversity of the training data. (2) Hard Sample Adaptive Learning (HSAL), which introduces a hardness-aware optimization strategy that adjusts feature distances based on textual semantic labels, encouraging the separation of hard positives and drawing hard negatives closer in the embedding space to enhance the model's discriminative capability and robustness to hard samples. Extensive experiments on multiple CC-ReID benchmarks demonstrate the effectiveness of our approach and highlight the potential of multimodal-guided hard sample generation and learning for robust CC-ReID. Notably, HSAL significantly accelerates the convergence of the targeted learning procedure and achieves state-of-the-art performance on both PRCC and LTCC datasets. The code is available at this https URL.
Abstract (translated)
难例在人员再识别（ReID）任务中，特别是在换衣人员再识别（CC-ReID）中提出了重大挑战。它们内在的模棱两可或相似性，加上缺乏明确定义，构成了一个根本性的瓶颈。这些问题不仅限制了针对性学习策略的设计，也削弱了模型在衣物变化或视角变换情况下的鲁棒性。 在这篇论文中，我们提出了一种新颖的多模态引导难例生成和学习（HSGL）框架，这是首次尝试统一文本和视觉模态以明确定义、生成并优化难例的方法，并在此统一流程中进行。HSGL包含两个核心组件： 1. **双粒度难例生成**（DGHSG），利用多模态线索来合成语义一致的样本，包括粗略级和细粒度正负难例，有效增强了训练数据的难度和多样性。 2. **难例自适应学习**（HSAL），引入了一种基于困难感知的优化策略，根据文本语义标签调整特征距离，鼓励在嵌入空间中将难正例分离并拉近难负面，以增强模型对难例的判别能力和鲁棒性。 通过多个CC-ReID基准数据集上的广泛实验，证明了我们方法的有效性和多模态引导难例生成与学习对于稳健的CC-ReID具有潜在价值。特别地，HSAL显著加速了针对性学习过程的收敛，并在PRCC和LTCC数据集中达到了最先进的性能。代码可在提供的网址上获取。
URL
https://arxiv.org/abs/2507.11119
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Mind the Gap: Bridging Occlusion in Gait Recognition via Residual Gap Correction
2025-07-15 04:45:14Ayush Gupta, Siyuan Huang, Rama ChellappaAbstract
Gait is becoming popular as a method of person re-identification because of its ability to identify people at a distance. However, most current works in gait recognition do not address the practical problem of occlusions. Among those which do, some require paired tuples of occluded and holistic sequences, which are impractical to collect in the real world. Further, these approaches work on occlusions but fail to retain performance on holistic inputs. To address these challenges, we propose RG-Gait, a method for residual correction for occluded gait recognition with holistic retention. We model the problem as a residual learning task, conceptualizing the occluded gait signature as a residual deviation from the holistic gait representation. Our proposed network adaptively integrates the learned residual, significantly improving performance on occluded gait sequences without compromising the holistic recognition accuracy. We evaluate our approach on the challenging Gait3D, GREW and BRIAR datasets and show that learning the residual can be an effective technique to tackle occluded gait recognition with holistic retention.
Abstract (translated)
步态识别作为一种重新识别人员的方法正因其能够在远距离识别人的能力而变得越来越流行。然而，目前大多数关于步态识别的工作并没有解决实际中的遮挡问题。在少数研究遮挡问题的现有工作中，一些方法需要成对的遮挡序列和完整序列数据，这种需求在现实世界中难以获取。此外，这些方法虽然能处理遮挡情况，但在面对无遮挡的情况时性能表现不佳。 为了解决这些问题，我们提出了RG-Gait（残差矫正步态识别）这一方法，旨在解决遮挡下的步态识别问题，并保持对完整序列的识别准确性。我们将该问题视为一个残差学习任务，将被遮挡的步态特征看作是从完整的步态表示中偏离的一个残差。我们的网络能够自适应地整合所学得的残差信息，在不牺牲完全视图下步态识别准确性的前提下，显著提高对部分遮挡序列的识别性能。 我们在具有挑战性的Gait3D、GREW和BRIAR数据集上评估了我们提出的方法，并展示了学习残差可以作为一种有效的技术来处理带有完整视图保留能力的被遮挡步态识别问题。
URL
https://arxiv.org/abs/2507.10978
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Occlusion-Guided Feature Purification Learning via Reinforced Knowledge Distillation for Occluded Person Re-Identification
2025-07-11 12:05:30Yufei Zheng, Wenjun Wang, Wenjun Gan, Jiawei LiuAbstract
Occluded person re-identification aims to retrieve holistic images based on occluded ones. Existing methods often rely on aligning visible body parts, applying occlusion augmentation, or complementing missing semantics using holistic images. However, they face challenges in handling diverse occlusion scenarios not seen during training and the issue of feature contamination from holistic images. To address these limitations, we propose Occlusion-Guided Feature Purification Learning via Reinforced Knowledge Distillation (OGFR), which simultaneously mitigates these challenges. OGFR adopts a teacher-student distillation architecture that effectively incorporates diverse occlusion patterns into feature representation while transferring the purified discriminative holistic knowledge from the holistic to the occluded branch through reinforced knowledge distillation. Specifically, an Occlusion-Aware Vision Transformer is designed to leverage learnable occlusion pattern embeddings to explicitly model such diverse occlusion types, thereby guiding occlusion-aware robust feature representation. Moreover, we devise a Feature Erasing and Purification Module within the holistic branch, in which an agent is employed to identify low-quality patch tokens of holistic images that contain noisy negative information via deep reinforcement learning, and substitute these patch tokens with learnable embedding tokens to avoid feature contamination and further excavate identity-related discriminative clues. Afterward, with the assistance of knowledge distillation, the student branch effectively absorbs the purified holistic knowledge to precisely learn robust representation regardless of the interference of occlusions.
Abstract (translated)
遮挡人物重识别的目标是根据带有遮挡的图像检索出完整的图像。现有的方法通常依赖于对可见身体部位进行对齐，应用遮挡增强处理，或使用完整图象来补充缺失的语义信息。然而，这些方法在面对训练期间未见过的各种遮挡场景和来自完整图像特征污染的问题时面临挑战。 为了克服这些限制，我们提出了通过强化知识蒸馏实现的遮挡引导特征净化学习（OGFR），这种方法同时缓解了上述挑战。OGFR采用了一种教师-学生蒸馏架构，该架构能够有效地将各种遮挡模式整合进特征表示中，并通过强化知识蒸馏从完整分支向遮挡分支传输经过净化的鉴别性完整图像知识。 具体而言，设计了一个感知遮挡的视觉变换器（Occlusion-Aware Vision Transformer），利用可学习的遮挡模式嵌入来显式建模各种多样的遮挡类型，从而引导生成针对遮挡敏感且鲁棒的特征表示。此外，在完整的分支中，我们开发了一个特征擦除和净化模块，其中使用代理通过深度强化学习识别包含嘈杂负信息的完整图像低质量补丁标记，并用可学习嵌入标记替换这些补丁标记以避免特征污染并进一步挖掘与身份相关的鉴别性线索。 最后，在知识蒸馏的帮助下，学生分支能够有效吸收经过净化的完整图像知识，从而无论遮挡干扰如何都能精确地学习到鲁棒表示。
URL
https://arxiv.org/abs/2507.08520
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KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos
2025-07-10 03:15:57Jinseong Kim, Junghoon Song, Gyeongseon Baek, Byeongjoon NohAbstract
We propose \textbf{KeyRe-ID}, a keypoint-guided video-based person re-identification framework consisting of global and local branches that leverage human keypoints for enhanced spatiotemporal representation learning. The global branch captures holistic identity semantics through Transformer-based temporal aggregation, while the local branch dynamically segments body regions based on keypoints to generate fine-grained, part-aware features. Extensive experiments on MARS and iLIDS-VID benchmarks demonstrate state-of-the-art performance, achieving 91.73\% mAP and 97.32\% Rank-1 accuracy on MARS, and 96.00\% Rank-1 and 100.0\% Rank-5 accuracy on iLIDS-VID. The code for this work will be publicly available on GitHub upon publication.
Abstract (translated)
我们提出了**KeyRe-ID**，这是一种基于关键点的视频人物重识别框架，该框架由全局和局部两部分组成，并利用人体关键点进行增强的空间时间表示学习。全局分支通过基于Transformer的时间聚合捕捉整体身份语义，而局部分支则根据关键点动态分割身体区域以生成细粒度、部件感知特征。 在MARS和iLIDS-VID基准测试中进行了广泛的实验，证明了该方法的先进性能，在MARS上达到了91.73% mAP和97.32% Rank-1准确率，在iLIDS-VID上则分别实现了96.00% Rank-1和100.0% Rank-5的准确性。 此工作的代码将在发布后在GitHub上公开。
URL
https://arxiv.org/abs/2507.07393
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Following the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence
2025-07-02 09:10:33Robert Aufschl\"ager, Youssef Shoeb, Azarm Nowzad, Michael Heigl, Fabian Bally, Martin SchrammAbstract
The collection and release of street-level recordings as Open Data play a vital role in advancing autonomous driving systems and AI research. However, these datasets pose significant privacy risks, particularly for pedestrians, due to the presence of Personally Identifiable Information (PII) that extends beyond biometric traits such as faces. In this paper, we present cRID, a novel cross-modal framework combining Large Vision-Language Models, Graph Attention Networks, and representation learning to detect textual describable clues of PII and enhance person re-identification (Re-ID). Our approach focuses on identifying and leveraging interpretable features, enabling the detection of semantically meaningful PII beyond low-level appearance cues. We conduct a systematic evaluation of PII presence in person image datasets. Our experiments show improved performance in practical cross-dataset Re-ID scenarios, notably from Market-1501 to CUHK03-np (detected), highlighting the framework's practical utility. Code is available at this https URL.
Abstract (translated)
作为开放数据的一部分，街道级别的记录在推进自动驾驶系统和人工智能研究中发挥着至关重要的作用。然而，这些数据集存在显著的隐私风险，尤其是对行人而言，因为其中包含的个人识别信息（PII）超出了面部等生物特征之外的信息。 本文介绍了cRID，这是一种结合了大型视觉-语言模型、图注意力网络以及表示学习的跨模态框架，用于检测文本描述性线索中的个人识别信息，并增强人员重识别（Re-ID）。我们的方法专注于识别和利用可解释的特性，从而能够发现超越低级外观提示的语义上有意义的PII。我们对人物图像数据集中个人识别信息的存在进行了系统的评估。 实验结果显示，在实际跨数据集人员重识别场景中性能有所提升，特别是从Market-1501到CUHK03-np（检测）的数据迁移上表现尤为突出，这表明了框架的实际应用价值。代码可在上述链接中获得。
URL
https://arxiv.org/abs/2507.01504
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SCING:Towards More Efficient and Robust Person Re-Identification through Selective Cross-modal Prompt Tuning
2025-07-01 07:21:31Yunfei Xie, Yuxuan Cheng, Juncheng Wu, Haoyu Zhang, Yuyin Zhou, Shoudong HanAbstract
Recent advancements in adapting vision-language pre-training models like CLIP for person re-identification (ReID) tasks often rely on complex adapter design or modality-specific tuning while neglecting cross-modal interaction, leading to high computational costs or suboptimal alignment. To address these limitations, we propose a simple yet effective framework named Selective Cross-modal Prompt Tuning (SCING) that enhances cross-modal alignment and robustness against real-world perturbations. Our method introduces two key innovations: Firstly, we proposed Selective Visual Prompt Fusion (SVIP), a lightweight module that dynamically injects discriminative visual features into text prompts via a cross-modal gating mechanism. Moreover, the proposed Perturbation-Driven Consistency Alignment (PDCA) is a dual-path training strategy that enforces invariant feature alignment under random image perturbations by regularizing consistency between original and augmented cross-modal embeddings. Extensive experiments are conducted on several popular benchmarks covering Market1501, DukeMTMC-ReID, Occluded-Duke, Occluded-REID, and P-DukeMTMC, which demonstrate the impressive performance of the proposed method. Notably, our framework eliminates heavy adapters while maintaining efficient inference, achieving an optimal trade-off between performance and computational overhead. The code will be released upon acceptance.
Abstract (translated)
近期，针对利用如CLIP等视觉-语言预训练模型进行人员重新识别（ReID）任务的研究进展，通常依赖于复杂的适配器设计或特定模态的调优方法，但这些方法往往忽视了跨模态交互的重要性，导致计算成本高昂或者对齐效果不佳。为了解决这些问题，我们提出了一种简单而有效的框架，名为选择性跨模态提示调整（SCING），该框架旨在增强跨模态对齐能力并提升在现实世界扰动下的鲁棒性。 我们的方法引入了两个关键创新点： 1. **选择性视觉提示融合（SVIP）**：这是一种轻量级模块，通过一个跨模态门控机制动态地将具有区分性的视觉特征注入文本提示中。 2. **扰动驱动的一致性对齐（PDCA）**：这是一种双路径训练策略，通过对原始和增强后的跨模态嵌入之间的一致性进行正则化来强制执行在随机图像扰动下的不变特性对齐。 我们在多个流行基准数据集上进行了广泛的实验，包括Market1501、DukeMTMC-ReID、Occluded-Duke、Occluded-REID和P-DukeMTMC。实验结果表明了所提出方法的卓越性能。值得注意的是，我们的框架在消除重适配器的同时保持高效的推理能力，从而实现了性能与计算开销之间的最佳平衡。 该代码将在论文被接受后发布。
URL
https://arxiv.org/abs/2507.00506
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Online Human Action Detection during Escorting
2025-06-30 07:25:31Siddhartha Mondal, Avik Mitra, Chayan SarkarAbstract
The deployment of robot assistants in large indoor spaces has seen significant growth, with escorting tasks becoming a key application. However, most current escorting robots primarily rely on navigation-focused strategies, assuming that the person being escorted will follow without issue. In crowded environments, this assumption often falls short, as individuals may struggle to keep pace, become obstructed, get distracted, or need to stop unexpectedly. As a result, conventional robotic systems are often unable to provide effective escorting services due to their limited understanding of human movement dynamics. To address these challenges, an effective escorting robot must continuously detect and interpret human actions during the escorting process and adjust its movement accordingly. However, there is currently no existing dataset designed specifically for human action detection in the context of escorting. Given that escorting often occurs in crowded environments, where other individuals may enter the robot's camera view, the robot also needs to identify the specific human it is escorting (the subject) before predicting their actions. Since no existing model performs both person re-identification and action prediction in real-time, we propose a novel neural network architecture that can accomplish both tasks. This enables the robot to adjust its speed dynamically based on the escortee's movements and seamlessly resume escorting after any disruption. In comparative evaluations against strong baselines, our system demonstrates superior efficiency and effectiveness, showcasing its potential to significantly improve robotic escorting services in complex, real-world scenarios.
Abstract (translated)
在大型室内空间中部署机器人助手已经取得了显著的增长，其中引导任务已成为关键应用之一。然而，大多数现有的引导机器人主要依赖于导航策略，假设被引导的人能够顺利跟随而没有任何问题。但在拥挤的环境中，这一假设往往无法实现，因为个体可能会遇到跟不上步伐、被阻挡、分心或意外停下等问题。因此，传统的机器人系统由于对人类运动动态的理解有限，通常难以提供有效的引导服务。 为了解决这些问题，一个高效的引导机器人必须能够在引导过程中持续检测和解释人的动作，并根据这些信息调整自己的移动方式。然而，目前还没有专门针对引导场景设计的人类行为检测数据集。考虑到引导任务往往发生在拥挤的环境中，在这种情况下，其他个体可能会进入机器人的摄像机视野内，因此机器人需要在预测被引导人（目标）的行为之前识别出特定的这个人。由于现有的模型无法同时实现实时的人物再识别和动作预测，我们提出了一种新型神经网络架构来实现这两个任务。这使得机器人能够根据跟随者的移动情况动态调整速度，并且能够在任何干扰后无缝地继续进行引导。 在与强大基准方法进行比较评估中，我们的系统展示了更高的效率和有效性，表明它有潜力显著提高复杂现实场景中的机器人引导服务的水平。
URL
https://arxiv.org/abs/2506.23573
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AG-VPReID 2025: Aerial-Ground Video-based Person Re-identification Challenge Results
2025-06-28 10:45:30Kien Nguyen, Clinton Fookes, Sridha Sridharan, Huy Nguyen, Feng Liu, Xiaoming Liu, Arun Ross, Dana Michalski, Tam\'as Endrei, Ivan DeAndres-Tame, Ruben Tolosana, Ruben Vera-Rodriguez, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia, Zijing Gong, Yuhao Wang, Xuehu Liu, Pingping Zhang, Md Rashidunnabi, Hugo Proen\c{c}a, Kailash A. Hambarde, Saeid RezaeiAbstract
Person re-identification (ReID) across aerial and ground vantage points has become crucial for large-scale surveillance and public safety applications. Although significant progress has been made in ground-only scenarios, bridging the aerial-ground domain gap remains a formidable challenge due to extreme viewpoint differences, scale variations, and occlusions. Building upon the achievements of the AG-ReID 2023 Challenge, this paper introduces the AG-VPReID 2025 Challenge - the first large-scale video-based competition focused on high-altitude (80-120m) aerial-ground ReID. Constructed on the new AG-VPReID dataset with 3,027 identities, over 13,500 tracklets, and approximately 3.7 million frames captured from UAVs, CCTV, and wearable cameras, the challenge featured four international teams. These teams developed solutions ranging from multi-stream architectures to transformer-based temporal reasoning and physics-informed modeling. The leading approach, X-TFCLIP from UAM, attained 72.28% Rank-1 accuracy in the aerial-to-ground ReID setting and 70.77% in the ground-to-aerial ReID setting, surpassing existing baselines while highlighting the dataset's complexity. For additional details, please refer to the official website at this https URL.
Abstract (translated)
人员再识别（ReID）在空中和地面视角之间的跨域应用对于大规模监控和公共安全至关重要。尽管仅限于地面场景的进展显著，但由于极端视点差异、尺度变化以及遮挡问题，跨越空地领域的挑战依然巨大。基于AG-ReID 2023挑战赛的成绩，本文介绍了AG-VPReID 2025挑战赛——首个专注于高空（80至120米）空中与地面再识别的大规模视频竞赛。该挑战赛建立在新发布的AG-VPReID数据集之上，该数据集包含3,027个身份、超过13,500条轨迹片段以及约370万帧图像，这些图像由无人机、CCTV和可穿戴相机采集而成。四支国际团队参与了此次挑战赛，并开发了一系列解决方案，包括多流架构到基于Transformer的时间推理及物理信息建模技术。 领先的方法是UAM提出的X-TFCLIP，在空中转地面再识别任务中实现了72.28%的Rank-1准确率，在地面向空中再识别任务中则达到了70.77%，这一成绩超越了现有基线，同时展示了数据集的独特复杂性。欲了解更多信息，请参阅官方网址：[this https URL]（请将方括号中的内容替换为实际链接）。
URL
https://arxiv.org/abs/2506.22843
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DeSPITE: Exploring Contrastive Deep Skeleton-Pointcloud-IMU-Text Embeddings for Advanced Point Cloud Human Activity Understanding
2025-06-16 18:18:44Thomas Kreutz, Max M\"uhlh\"auser, Alejandro Sanchez GuineaAbstract
Despite LiDAR (Light Detection and Ranging) being an effective privacy-preserving alternative to RGB cameras to perceive human activities, it remains largely underexplored in the context of multi-modal contrastive pre-training for human activity understanding (e.g., human activity recognition (HAR), retrieval, or person re-identification (RE-ID)). To close this gap, our work explores learning the correspondence between LiDAR point clouds, human skeleton poses, IMU data, and text in a joint embedding space. More specifically, we present DeSPITE, a Deep Skeleton-Pointcloud-IMU-Text Embedding model, which effectively learns a joint embedding space across these four modalities through noise contrastive estimation. At the heart of our empirical exploration, we have combined the existing LIPD and Babel datasets, which enabled us to synchronize data of all four modalities, allowing us to explore the learning of a new joint embedding space. Our experiments demonstrate novel human activity understanding tasks for point cloud sequences enabled through DeSPITE, including Skeleton<->Pointcloud<->IMU matching, retrieval, and temporal moment retrieval. Furthermore, we show that DeSPITE is an effective pre-training strategy for point cloud HAR through experiments in MSR-Action3D and HMPEAR.
Abstract (translated)
尽管激光雷达（LiDAR，Light Detection And Ranging）作为一种有效的隐私保护替代方案，在感知人类活动方面比RGB相机更加有效，但在多模态对比预训练以理解人类活动中（例如，人体动作识别(Human Activity Recognition, HAR)、检索或行人再识别(Person Re-Identification, RE-ID)等任务）的应用仍然较少。为了弥补这一不足，我们的工作探索了在联合嵌入空间中学习激光雷达点云、人体骨架姿势、惯性测量单元(IMU)数据和文本之间对应关系的方法。 具体来说，我们提出了DeSPITE（Deep Skeleton-Pointcloud-IMU-Text Embedding）模型，这是一种深度骨架-点云-IMU-文本嵌入模型。该模型能够通过噪声对比估计方法有效地学习这四种模态之间的联合嵌入空间。在我们的经验研究中，我们将现有的LIPD和Babel数据集结合起来使用，从而实现了所有四类数据的同步，使我们能够在新的联合嵌入空间中进行探索。 实验表明，DeSPITE为点云序列中的新的人体活动理解任务提供了可能，包括骨架<->点云<->IMU匹配、检索以及时间段检索。此外，通过在MSR-Action3D和HMPEAR数据集上的实验，我们证明了DeSPITE是一种有效的预训练策略，可以应用于基于点云的动作识别任务中。
URL
https://arxiv.org/abs/2506.13897
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Evolution of ReID: From Early Methods to LLM Integration
2025-06-16 02:03:46Amran Bhuiyan, Mizanur Rahman, Md Tahmid Rahman Laskar, Aijun An, Jimmy Xiangji HuangAbstract
Person re-identification (ReID) has evolved from handcrafted feature-based methods to deep learning approaches and, more recently, to models incorporating large language models (LLMs). Early methods struggled with variations in lighting, pose, and viewpoint, but deep learning addressed these issues by learning robust visual features. Building on this, LLMs now enable ReID systems to integrate semantic and contextual information through natural language. This survey traces that full evolution and offers one of the first comprehensive reviews of ReID approaches that leverage LLMs, where textual descriptions are used as privileged information to improve visual matching. A key contribution is the use of dynamic, identity-specific prompts generated by GPT-4o, which enhance the alignment between images and text in vision-language ReID systems. Experimental results show that these descriptions improve accuracy, especially in complex or ambiguous cases. To support further research, we release a large set of GPT-4o-generated descriptions for standard ReID datasets. By bridging computer vision and natural language processing, this survey offers a unified perspective on the field's development and outlines key future directions such as better prompt design, cross-modal transfer learning, and real-world adaptability.
Abstract (translated)
人员重新识别（ReID）技术已经从基于手工制作特征的方法演进到深度学习方法，再到最近结合大型语言模型（LLMs）的模型。早期方法在处理光照、姿态和视角变化时遇到了困难，但深度学习通过学习鲁棒的视觉特征解决了这些问题。在此基础上，LLMs使得ReID系统能够通过自然语言整合语义和上下文信息。本次调查追溯了这一完整的发展历程，并对利用LLMs增强人员重新识别的方法进行了迄今为止最全面的回顾之一，其中文本描述被用作特权信息来提升视觉匹配性能。一个关键贡献是使用GPT-4o生成的身份特定动态提示，这增强了图像与文字之间的对齐，在视觉语言ReID系统中尤为有效。实验结果显示，这些描述在复杂或模棱两可的情况下特别提升了准确度。为了支持进一步的研究，我们发布了一大批针对标准ReID数据集的由GPT-4o生成的描述。通过将计算机视觉和自然语言处理领域结合在一起，这项调查提供了一个统一视角来审视该领域的进展，并概述了未来的关键发展方向，如更好的提示设计、跨模态迁移学习以及现实世界的适应性。
URL
https://arxiv.org/abs/2506.13039
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Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
2025-06-14 09:15:00Hyeonseo Lee, Juhyun Park, Jihyong Oh, Chanho EomAbstract
Person Re-identification (ReID) aims to retrieve images of the same individual captured across non-overlapping camera views, making it a critical component of intelligent surveillance systems. Traditional ReID methods assume that the training and test domains share similar characteristics and primarily focus on learning discriminative features within a given domain. However, they often fail to generalize to unseen domains due to domain shifts caused by variations in viewpoint, background, and lighting conditions. To address this issue, Domain-Adaptive ReID (DA-ReID) methods have been proposed. These approaches incorporate unlabeled target domain data during training and improve performance by aligning feature distributions between source and target domains. Domain-Generalizable ReID (DG-ReID) tackles a more realistic and challenging setting by aiming to learn domain-invariant features without relying on any target domain data. Recent methods have explored various strategies to enhance generalization across diverse environments, but the field remains relatively underexplored. In this paper, we present a comprehensive survey of DG-ReID. We first review the architectural components of DG-ReID including the overall setting, commonly used backbone networks and multi-source input configurations. Then, we categorize and analyze domain generalization modules that explicitly aim to learn domain-invariant and identity-discriminative representations. To examine the broader applicability of these techniques, we further conduct a case study on a related task that also involves distribution shifts. Finally, we discuss recent trends, open challenges, and promising directions for future research in DG-ReID. To the best of our knowledge, this is the first systematic survey dedicated to DG-ReID.
Abstract (translated)
人员重新识别（ReID）旨在从不重叠的摄像机视图中检索同一人的图像，是智能监控系统中的一个关键组成部分。传统的ReID方法假设训练和测试领域具有相似的特点，并主要集中在给定领域的判别特征学习上。然而，由于视角、背景和光照条件的变化，这些方法在面对未见领域时往往难以泛化。为了解决这一问题，域适应性人员重新识别（Domain-Adaptive ReID, DA-ReID）方法被提出。这类方法通过在训练过程中利用无标签的目标域数据来改进性能，并通过调整源域和目标域之间的特征分布实现这一点。相比之下，领域通用型人员重识别（Domain-Generalizable ReID, DG-ReID）旨在学习不依赖任何目标域数据的领域不变性特征，从而应对更现实且更具挑战性的场景设置。 最近的方法探索了各种策略以增强在多样化环境中的泛化能力，但该领域的研究相对较少。本文提供了一篇关于DG-ReID的全面综述。首先，我们回顾了DG-ReID的基本架构组件，包括整体设定、常用的骨干网络和多源输入配置。接下来，我们将分析专门用于学习领域不变性和身份判别表示的域泛化模块，并对其进行分类。为了考察这些技术在更广泛任务中的适用性，我们还对一个涉及分布变化的相关任务进行了案例研究。最后，我们讨论了DG-ReID领域的最新趋势、开放挑战以及未来的研究方向。 据我们所知，这是首次关于DG-ReID的系统性综述文章。
URL
https://arxiv.org/abs/2506.12413
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ReID5o: Achieving Omni Multi-modal Person Re-identification in a Single Model
2025-06-11 04:26:13Jialong Zuo, Yongtai Deng, Mengdan Tan, Rui Jin, Dongyue Wu, Nong Sang, Liang Pan, Changxin GaoAbstract
In real-word scenarios, person re-identification (ReID) expects to identify a person-of-interest via the descriptive query, regardless of whether the query is a single modality or a combination of multiple modalities. However, existing methods and datasets remain constrained to limited modalities, failing to meet this requirement. Therefore, we investigate a new challenging problem called Omni Multi-modal Person Re-identification (OM-ReID), which aims to achieve effective retrieval with varying multi-modal queries. To address dataset scarcity, we construct ORBench, the first high-quality multi-modal dataset comprising 1,000 unique identities across five modalities: RGB, infrared, color pencil, sketch, and textual description. This dataset also has significant superiority in terms of diversity, such as the painting perspectives and textual information. It could serve as an ideal platform for follow-up investigations in OM-ReID. Moreover, we propose ReID5o, a novel multi-modal learning framework for person ReID. It enables synergistic fusion and cross-modal alignment of arbitrary modality combinations in a single model, with a unified encoding and multi-expert routing mechanism proposed. Extensive experiments verify the advancement and practicality of our ORBench. A wide range of possible models have been evaluated and compared on it, and our proposed ReID5o model gives the best performance. The dataset and code will be made publicly available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2506.09385
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Cross-Spectral Body Recognition with Side Information Embedding: Benchmarks on LLCM and Analyzing Range-Induced Occlusions on IJB-MDF
2025-06-10 16:20:52Anirudh Nanduri, Siyuan Huang, Rama ChellappaAbstract
Vision Transformers (ViTs) have demonstrated impressive performance across a wide range of biometric tasks, including face and body recognition. In this work, we adapt a ViT model pretrained on visible (VIS) imagery to the challenging problem of cross-spectral body recognition, which involves matching images captured in the visible and infrared (IR) domains. Recent ViT architectures have explored incorporating additional embeddings beyond traditional positional embeddings. Building on this idea, we integrate Side Information Embedding (SIE) and examine the impact of encoding domain and camera information to enhance cross-spectral matching. Surprisingly, our results show that encoding only camera information - without explicitly incorporating domain information - achieves state-of-the-art performance on the LLCM dataset. While occlusion handling has been extensively studied in visible-spectrum person re-identification (Re-ID), occlusions in visible-infrared (VI) Re-ID remain largely underexplored - primarily because existing VI-ReID datasets, such as LLCM, SYSU-MM01, and RegDB, predominantly feature full-body, unoccluded images. To address this gap, we analyze the impact of range-induced occlusions using the IARPA Janus Benchmark Multi-Domain Face (IJB-MDF) dataset, which provides a diverse set of visible and infrared images captured at various distances, enabling cross-range, cross-spectral evaluations.
Abstract (translated)
视觉变压器（ViT）在包括面部和身体识别在内的多种生物特征任务中表现出色。在这项工作中，我们将一个预训练于可见光图像的ViT模型应用于跨谱系身体识别这一具有挑战性的问题上，该问题涉及到匹配可视域和红外域捕获的图像。最近的ViT架构探索了在传统位置嵌入之外添加额外嵌入的方法。在此基础上，我们整合了侧信息嵌入（SIE），并研究编码领域和相机信息对跨谱系匹配的影响。令人惊讶的是，我们的结果显示，在LLCM数据集上，仅通过编码相机信息——而无需明确地将领域信息纳入考虑内——即可达到最先进的性能。 尽管在可见光谱范围内的人员重新识别（Re-ID）中已经广泛研究了遮挡处理方法，但针对可见-红外（VI）Re-ID中的遮挡问题尚未得到充分探索。这主要是因为现有的VI-ReID数据集（如LLCM、SYSU-MM01和RegDB），主要包含的是全身且未被遮挡的图像。 为了解决这一差距，我们利用IARPA Janus基准多域面部识别（IJB-MDF）数据集分析了由范围引起的遮挡影响。该数据集提供了一组多样化的可见光和红外图像，在不同距离下拍摄，支持跨范围、跨谱系评估。
URL
https://arxiv.org/abs/2506.08953
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Person Re-Identification System at Semantic Level based on Pedestrian Attributes Ontology
2025-06-04 16:34:31Ngoc Q. Ly, Hieu N. M. Cao, Thi T. NguyenAbstract
Person Re-Identification (Re-ID) is a very important task in video surveillance systems such as tracking people, finding people in public places, or analysing customer behavior in supermarkets. Although there have been many works to solve this problem, there are still remaining challenges such as large-scale datasets, imbalanced data, viewpoint, fine grained data (attributes), the Local Features are not employed at semantic level in online stage of Re-ID task, furthermore, the imbalanced data problem of attributes are not taken into consideration. This paper has proposed a Unified Re-ID system consisted of three main modules such as Pedestrian Attribute Ontology (PAO), Local Multi-task DCNN (Local MDCNN), Imbalance Data Solver (IDS). The new main point of our Re-ID system is the power of mutual support of PAO, Local MDCNN and IDS to exploit the inner-group correlations of attributes and pre-filter the mismatch candidates from Gallery set based on semantic information as Fashion Attributes and Facial Attributes, to solve the imbalanced data of attributes without adjusting network architecture and data augmentation. We experimented on the well-known Market1501 dataset. The experimental results have shown the effectiveness of our Re-ID system and it could achieve the higher performance on Market1501 dataset in comparison to some state-of-the-art Re-ID methods.
Abstract (translated)
人重新识别（Re-ID）是视频监控系统中的一个非常重要的任务，例如跟踪人员、在公共场所寻找人员或分析超市中顾客的行为。尽管已经有许多研究致力于解决这一问题，但仍存在一些挑战，如大规模数据集的处理、数据不平衡、视角变化以及细粒度数据（属性）等问题。此外，在人重新识别任务的在线阶段，局部特征并未被用于语义层面的应用，同时，属性的数据不平衡问题也未得到充分考虑。 本文提出了一种统一的人重新识别系统，该系统由三个主要模块组成：行人属性本体论（PAO）、局部多任务深度卷积神经网络（Local MDCNN）和数据不平衡求解器（IDS）。我们的人重新识别系统的创新点在于，这三个模块——即PAO、Local MDCNN 和 IDS 能够相互支持，通过利用属性之间的内部分组相关性，并基于语义信息如时尚属性和面部属性对Gallery集合中的不匹配候选者进行预筛选，以解决属性数据不平衡问题而不调整网络架构或数据增强。 我们在著名的Market1501数据集上进行了实验。实验结果表明了我们的人重新识别系统的效果显著，在Market1501数据集上的性能优于一些最新的Re-ID方法。
URL
https://arxiv.org/abs/2506.04143
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