Re-identification
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MMReID-Bench: Unleashing the Power of MLLMs for Effective and Versatile Person Re-identification
2025-08-09 09:42:09Jinhao Li, Zijian Chen, Lirong Deng, Changbo Wang, Guangtao ZhaiAbstract
Person re-identification (ReID) aims to retrieve the images of an interested person in the gallery images, with wide applications in medical rehabilitation, abnormal behavior detection, and public security. However, traditional person ReID models suffer from uni-modal capability, leading to poor generalization ability in multi-modal data, such as RGB, thermal, infrared, sketch images, textual descriptions, etc. Recently, the emergence of multi-modal large language models (MLLMs) shows a promising avenue for addressing this problem. Despite this potential, existing methods merely regard MLLMs as feature extractors or caption generators, which do not fully unleash their reasoning, instruction-following, and cross-modal understanding capabilities. To bridge this gap, we introduce MMReID-Bench, the first multi-task multi-modal benchmark specifically designed for person ReID. The MMReID-Bench includes 20,710 multi-modal queries and gallery images covering 10 different person ReID tasks. Comprehensive experiments demonstrate the remarkable capabilities of MLLMs in delivering effective and versatile person ReID. Nevertheless, they also have limitations in handling a few modalities, particularly thermal and infrared data. We hope MMReID-Bench can facilitate the community to develop more robust and generalizable multimodal foundation models for person ReID.
Abstract (translated)
人员重识别（ReID）的目标是从图库图像中检索感兴趣人物的图片，其在医疗康复、异常行为检测和公共安全等领域有广泛应用。然而，传统的人员ReID模型存在单一模态能力的问题，导致其在RGB、热成像、红外、素描图像以及文本描述等多模态数据上的泛化能力较差。最近，多模态大型语言模型（MLLMs）的出现为解决这一问题提供了有前景的方法。尽管潜力巨大，现有方法仅将MLLMs视为特征提取器或标题生成器，未能充分发挥其推理、指令遵循及跨模态理解的能力。为弥补这一差距，我们引入了MMReID-Bench，这是首个专门为人员ReID设计的多任务多模态基准平台。MMReID-Bench包含了20,710个多模态查询和图库图片，并涵盖了10种不同的人员ReID任务。综合实验展示了MLLMs在提供有效且多功能人员ReID方面的显著能力，然而它们在处理某些特定模式数据（尤其是热成像和红外数据）时仍存在局限性。我们希望通过MMReID-Bench能够促进社区开发出更加稳健且具有泛化性的多模态基础模型以应对人员ReID挑战。
URL
https://arxiv.org/abs/2508.06908
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Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification
2025-08-09 05:10:22Taha Mustapha Nehdi, Nairouz Mrabah, Atif Belal, Marco Pedersoli, Eric GrangerAbstract
Adapting person re-identification (reID) models to new target environments remains a challenging problem that is typically addressed using unsupervised domain adaptation (UDA) methods. Recent works show that when labeled data originates from several distinct sources (e.g., datasets and cameras), considering each source separately and applying multi-source domain adaptation (MSDA) typically yields higher accuracy and robustness compared to blending the sources and performing conventional UDA. However, state-of-the-art MSDA methods learn domain-specific backbone models or require access to source domain data during adaptation, resulting in significant growth in training parameters and computational cost. In this paper, a Source-free Adaptive Gated Experts (SAGE-reID) method is introduced for person reID. Our SAGE-reID is a cost-effective, source-free MSDA method that first trains individual source-specific low-rank adapters (LoRA) through source-free UDA. Next, a lightweight gating network is introduced and trained to dynamically assign optimal merging weights for fusion of LoRA experts, enabling effective cross-domain knowledge transfer. While the number of backbone parameters remains constant across source domains, LoRA experts scale linearly but remain negligible in size (<= 2% of the backbone), reducing both the memory consumption and risk of overfitting. Extensive experiments conducted on three challenging benchmarks: Market-1501, DukeMTMC-reID, and MSMT17 indicate that SAGE-reID outperforms state-of-the-art methods while being computationally efficient.
Abstract (translated)
将人员再识别（Re-ID）模型适应到新的目标环境中仍然是一个挑战性问题，通常通过无监督领域自适应（UDA）方法来解决。最近的研究表明，当标注数据来自多个不同的来源时（例如，不同数据集或摄像机），分别考虑每个源并应用多源领域自适应（MSDA）相比于混合这些源然后执行传统的UDA往往能获得更高的准确性和鲁棒性。然而，最先进的MSDA方法学习特定领域的骨干模型或是需要在调整过程中访问源域的数据，这会导致训练参数的显著增加和计算成本的上升。 本文介绍了一种名为“无源自适应专家门控”（SAGE-reID）的方法来解决人员再识别问题。我们的SAGE-reID是一种高效的、无需源数据的MSDA方法，首先通过无监督领域自适应的方式训练每个来源特异性的低秩适配器（LoRA）。接着引入了一个轻量级的门控网络，并对其进行训练以动态分配融合LoRA专家时的最佳合并权重，从而实现有效的跨域知识转移。虽然骨干参数的数量在不同源域中保持不变，但LoRA专家数量线性增长却仍然很小（不超过骨干模型大小的2%），这减少了内存消耗和过拟合的风险。 我们在三个具有挑战性的基准数据集上进行了广泛的实验：Market-1501、DukeMTMC-reID 和 MSMT17。结果表明，SAGE-reID在计算效率方面超越了现有的最先进方法，并且性能更优。
URL
https://arxiv.org/abs/2508.06831
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HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID
2025-08-07 05:34:14Yiyang Su, Yunping Shi, Feng Liu, Xiaoming LiuAbstract
Recently, research interest in person re-identification (ReID) has increasingly focused on video-based scenarios, which are essential for robust surveillance and security in varied and dynamic environments. However, existing video-based ReID methods often overlook the necessity of identifying and selecting the most discriminative features from both videos in a query-gallery pair for effective matching. To address this issue, we propose a novel Hierarchical and Adaptive Mixture of Biometric Experts (HAMoBE) framework, which leverages multi-layer features from a pre-trained large model (e.g., CLIP) and is designed to mimic human perceptual mechanisms by independently modeling key biometric features--appearance, static body shape, and dynamic gait--and adaptively integrating them. Specifically, HAMoBE includes two levels: the first level extracts low-level features from multi-layer representations provided by the frozen large model, while the second level consists of specialized experts focusing on long-term, short-term, and temporal features. To ensure robust matching, we introduce a new dual-input decision gating network that dynamically adjusts the contributions of each expert based on their relevance to the input scenarios. Extensive evaluations on benchmarks like MEVID demonstrate that our approach yields significant performance improvements (e.g., +13.0% Rank-1 accuracy).
Abstract (translated)
最近，关于人员重新识别（ReID）的研究兴趣越来越多地集中在基于视频的场景上，这些场景对于在多变和动态环境中进行稳健的监控和安全至关重要。然而，现有的基于视频的ReID方法往往忽视了从查询库对中的视频中选择最具区分性的特征以实现有效匹配的重要性。为了解决这个问题，我们提出了一种新颖的层次化和自适应生物识别专家混合（HAMoBE）框架，该框架利用预训练的大模型（如CLIP）提供的多层特征，并设计成模仿人类感知机制，独立建模关键生物识别特征——外观、静态身体形状以及动态步态——并自适应地整合这些特征。具体来说，HAMoBE包括两个层次：第一层级从冻结大模型提供的多层次表示中提取低级特征，而第二层级则包含专注于长期、短期和时序特征的专门专家。为了确保稳健匹配，我们引入了一种新的双输入决策门控网络，该网络可以根据每个专家与输入场景的相关性动态调整其贡献。在像MEVID这样的基准数据集上的广泛评估表明，我们的方法带来了显著的性能改进（例如，Rank-1准确率提高了+13.0%）。
URL
https://arxiv.org/abs/2508.05038
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Attribute Guidance With Inherent Pseudo-label For Occluded Person Re-identification
2025-08-07 03:13:24Rui Zhi, Zhen Yang, Haiyang ZhangAbstract
Person re-identification (Re-ID) aims to match person images across different camera views, with occluded Re-ID addressing scenarios where pedestrians are partially visible. While pre-trained vision-language models have shown effectiveness in Re-ID tasks, they face significant challenges in occluded scenarios by focusing on holistic image semantics while neglecting fine-grained attribute information. This limitation becomes particularly evident when dealing with partially occluded pedestrians or when distinguishing between individuals with subtle appearance differences. To address this limitation, we propose Attribute-Guide ReID (AG-ReID), a novel framework that leverages pre-trained models' inherent capabilities to extract fine-grained semantic attributes without additional data or annotations. Our framework operates through a two-stage process: first generating attribute pseudo-labels that capture subtle visual characteristics, then introducing a dual-guidance mechanism that combines holistic and fine-grained attribute information to enhance image feature extraction. Extensive experiments demonstrate that AG-ReID achieves state-of-the-art results on multiple widely-used Re-ID datasets, showing significant improvements in handling occlusions and subtle attribute differences while maintaining competitive performance on standard Re-ID scenarios.
Abstract (translated)
人再识别（Re-ID）的目标是在不同摄像机视角中匹配人物图像，而遮挡下的再识别则专门处理行人部分可见的场景。尽管预训练的视觉-语言模型在人再识别任务中表现出色，但在处理遮挡场景时却面临着重大挑战，因为它们过于关注整体图像语义，忽视了细粒度属性信息的重要性。这一限制尤其明显地表现在处理部分被遮挡的行人或区分那些外表差异微妙的人物时。 为了解决这个问题，我们提出了Attribute-Guide ReID (AG-ReID)，这是一个新颖的框架，它利用预训练模型固有的能力来提取细粒度语义属性，而无需额外的数据标注。我们的框架通过一个两阶段的过程运作：首先生成能够捕捉细微视觉特征的属性伪标签，然后引入一种双重引导机制，将整体和细粒度属性信息相结合，以增强图像特征的提取。 广泛的实验表明，AG-ReID在多个广泛使用的Re-ID数据集上取得了最先进的结果，在处理遮挡及微妙属性差异方面表现出显著改进的同时，也保持了对标准Re-ID场景的竞争性能。
URL
https://arxiv.org/abs/2508.04998
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CLIPVehicle: A Unified Framework for Vision-based Vehicle Search
2025-08-06 06:36:44Likai Wang, Ruize Han, Xiangqun Zhang, Wei FengAbstract
Vehicles, as one of the most common and significant objects in the real world, the researches on which using computer vision technologies have made remarkable progress, such as vehicle detection, vehicle re-identification, etc. To search an interested vehicle from the surveillance videos, existing methods first pre-detect and store all vehicle patches, and then apply vehicle re-identification models, which is resource-intensive and not very practical. In this work, we aim to achieve the joint detection and re-identification for vehicle search. However, the conflicting objectives between detection that focuses on shared vehicle commonness and re-identification that focuses on individual vehicle uniqueness make it challenging for a model to learn in an end-to-end system. For this problem, we propose a new unified framework, namely CLIPVehicle, which contains a dual-granularity semantic-region alignment module to leverage the VLMs (Vision-Language Models) for vehicle discrimination modeling, and a multi-level vehicle identification learning strategy to learn the identity representation from global, instance and feature levels. We also construct a new benchmark, including a real-world dataset CityFlowVS, and two synthetic datasets SynVS-Day and SynVS-All, for vehicle search. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods of both vehicle Re-ID and person search tasks.
Abstract (translated)
车辆作为现实世界中最常见和最重要的对象之一，利用计算机视觉技术对其进行的研究已取得了显著的进展，例如车辆检测、车辆重识别等。为了从监控视频中搜索感兴趣的车辆，现有的方法通常先进行预检测并将所有车辆补丁存储起来，然后再应用车辆重识别模型，这种方法资源消耗大且不够实用。在本研究中，我们的目标是实现联合检测和重识别以用于车辆搜索。然而，检测侧重于共享的车辆共性而重识别则侧重于个体车辆的独特性，这种矛盾的目标使得在一个端到端系统中学习成为一个挑战。 针对这个问题，我们提出了一种新的统一框架，名为CLIPVehicle，该框架包含一个双粒度语义区域对齐模块，利用视觉语言模型（VLMs）进行车辆区分建模，并且采用多级车辆识别学习策略来从全局、实例和特征级别上学习身份表示。此外，我们还构建了一个新的基准，包括一个现实世界的数据集CityFlowVS以及两个合成数据集SynVS-Day和SynVS-All，用于车辆搜索任务。 通过广泛的实验结果证明，我们的方法在车辆重识别和人员检索任务中均超过了当前最先进的方法。
URL
https://arxiv.org/abs/2508.04120
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CORE-ReID V2: Advancing the Domain Adaptation for Object Re-Identification with Optimized Training and Ensemble Fusion
2025-08-06 02:57:09Trinh Quoc Nguyen, Oky Dicky Ardiansyah Prima, Syahid Al Irfan, Hindriyanto Dwi Purnomo, Radius TanoneAbstract
This study presents CORE-ReID V2, an enhanced framework building upon CORE-ReID. The new framework extends its predecessor by addressing Unsupervised Domain Adaptation (UDA) challenges in Person ReID and Vehicle ReID, with further applicability to Object ReID. During pre-training, CycleGAN is employed to synthesize diverse data, bridging image characteristic gaps across different domains. In the fine-tuning, an advanced ensemble fusion mechanism, consisting of the Efficient Channel Attention Block (ECAB) and the Simplified Efficient Channel Attention Block (SECAB), enhances both local and global feature representations while reducing ambiguity in pseudo-labels for target samples. Experimental results on widely used UDA Person ReID and Vehicle ReID datasets demonstrate that the proposed framework outperforms state-of-the-art methods, achieving top performance in Mean Average Precision (mAP) and Rank-k Accuracy (Top-1, Top-5, Top-10). Moreover, the framework supports lightweight backbones such as ResNet18 and ResNet34, ensuring both scalability and efficiency. Our work not only pushes the boundaries of UDA-based Object ReID but also provides a solid foundation for further research and advancements in this domain. Our codes and models are available at this https URL.
Abstract (translated)
这项研究提出了CORE-ReID V2，这是一个在原有的CORE-ReID基础上增强的框架。新框架通过解决人再识别（Person ReID）和车辆再识别（Vehicle ReID）中的无监督领域适应（UDA）挑战，并进一步扩展到物体再识别（Object ReID），来改进其前身。在预训练阶段，CycleGAN被用来合成多样化的数据集，以弥合不同域之间图像特征的差距。在微调阶段，一个先进的集成融合机制由高效的通道注意力块（ECAB）和简化的高效通道注意力块（SECAB）组成，提高了局部和全局特征表示能力，并减少了目标样本伪标签中的不确定性。 实验结果表明，在广泛使用的UDA Person ReID和Vehicle ReID数据集上，所提出的框架超越了最先进的方法，在平均精度均值（mAP）和等级准确度（Top-1、Top-5、Top-10）方面取得了最佳性能。此外，该框架支持轻量级的骨干网络如ResNet18和ResNet34，确保了可扩展性和高效性。 我们的工作不仅推动了基于UDA的物体再识别领域的边界，并且为后续的研究和发展奠定了坚实的基础。我们提供的代码和模型可在以下网址获得：[此URL]。
URL
https://arxiv.org/abs/2508.04036
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Distribution-aware Knowledge Unification and Association for Non-exemplar Lifelong Person Re-identification
2025-08-05 14:44:29Shiben Liu, Mingyue Xu, Huijie Fan, Qiang Wang, Yandong Tang, Zhi HanAbstract
Lifelong person re-identification (LReID) encounters a key challenge: balancing the preservation of old knowledge with adaptation to new information. Existing LReID methods typically employ knowledge distillation to enforce representation alignment. However, these approaches ignore two crucial aspects: specific distribution awareness and cross-domain unified knowledge learning, both of which are essential for addressing this challenge. To overcome these limitations, we propose a novel distribution-aware knowledge unification and association (DKUA) framework where domain-style modeling is performed for each instance to propagate domain-specific representations, enhancing anti-forgetting and generalization capacity. Specifically, we design a distribution-aware model to transfer instance-level representations of the current domain into the domain-specific representations with the different domain styles, preserving learned knowledge without storing old samples. Next, we propose adaptive knowledge consolidation (AKC) to dynamically generate the unified representation as a cross-domain representation center. To further mitigate forgetting, we develop a unified knowledge association (UKA) mechanism, which explores the unified representation as a bridge to explicitly model inter-domain associations, reducing inter-domain gaps. Finally, distribution-based knowledge transfer (DKT) is proposed to prevent the current domain distribution from deviating from the cross-domain distribution center, improving adaptation capacity. Experimental results show our DKUA outperforms the existing methods by 7.6%/5.3% average mAP/R@1 improvement on anti-forgetting and generalization capacity, respectively. Our code will be publicly released.
Abstract (translated)
终身人员重识别（LReID）面临的主要挑战是如何在保持旧知识的同时适应新信息。现有的LReID方法通常使用知识蒸馏来强制执行表示对齐，但这些方法忽略了两个关键方面：特定分布感知和跨域统一知识学习，这两点对于解决这一挑战至关重要。为了克服这些限制，我们提出了一种新颖的、基于分布感知的知识统合与关联（DKUA）框架，在该框架中为每个实例进行领域风格建模以传播领域特有表示，从而增强抗遗忘能力和泛化能力。 具体而言，我们设计了一个基于分布感知的模型，将当前领域的实例级别表示转化为具有不同领域风格的特定领域表示，而无需存储旧样本即可保持已学知识。接下来，我们提出了自适应知识巩固（AKC）机制，用于动态生成跨域表示中心作为统一表示。为了进一步减轻遗忘问题，我们开发了一种统一的知识关联（UKA）机制，该机制利用统一表示作为桥梁来显式地建模跨领域关联，减少跨领域的差距。 最后，我们提出了基于分布的知识转移（DKT），以防止当前领域的分布偏离跨域的中心分布，从而提高适应能力。实验结果表明，在抗遗忘能力和泛化能力方面，我们的DKUA方法分别比现有方法提高了7.6%和5.3%的平均mAP/R@1。 我们的代码将公开发布。
URL
https://arxiv.org/abs/2508.03516
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CORE-ReID: Comprehensive Optimization and Refinement through Ensemble fusion in Domain Adaptation for person re-identification
2025-08-05 04:25:03Trinh Quoc Nguyen, Oky Dicky Ardiansyah Prima, Katsuyoshi HottaAbstract
This study introduces a novel framework, "Comprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-identification (CORE-ReID)", to address an Unsupervised Domain Adaptation (UDA) for Person Re-identification (ReID). The framework utilizes CycleGAN to generate diverse data that harmonizes differences in image characteristics from different camera sources in the pre-training stage. In the fine-tuning stage, based on a pair of teacher-student networks, the framework integrates multi-view features for multi-level clustering to derive diverse pseudo labels. A learnable Ensemble Fusion component that focuses on fine-grained local information within global features is introduced to enhance learning comprehensiveness and avoid ambiguity associated with multiple pseudo-labels. Experimental results on three common UDAs in Person ReID demonstrate significant performance gains over state-of-the-art approaches. Additional enhancements, such as Efficient Channel Attention Block and Bidirectional Mean Feature Normalization mitigate deviation effects and adaptive fusion of global and local features using the ResNet-based model, further strengthening the framework. The proposed framework ensures clarity in fusion features, avoids ambiguity, and achieves high ac-curacy in terms of Mean Average Precision, Top-1, Top-5, and Top-10, positioning it as an advanced and effective solution for the UDA in Person ReID. Our codes and models are available at this https URL.
Abstract (translated)
这项研究提出了一种新颖的框架，即“通过领域适应进行集合融合以实现人员再识别（ReID）的全面优化和改进（CORE-ReID）”，旨在解决无监督领域适应（UDA）在人员重识别中的问题。该框架利用CycleGAN生成多样化的数据，在预训练阶段平衡来自不同摄像机源图像特征之间的差异。在微调阶段，基于一对教师-学生网络，该框架整合多视角特征进行多层次聚类以推导出多种伪标签。为了增强学习的全面性并避免与多个伪标签相关的模糊性，引入了一个可学习的集合融合组件，专注于从全局特征中提取细粒度局部信息。在三项常见的UDA实验中，Person ReID展示了显著优于现有方法的表现。此外，通过基于ResNet模型的有效通道注意力块和双向均值特征归一化进一步增强了框架的功能，以减少偏差效应，并适应性地融合全局与局部特征。所提出的框架确保了融合特征的清晰度，避免了模糊性，并在平均精度、Top-1、Top-5和Top-10指标上实现了高准确性，使其成为人员ReID领域UDA问题的一个先进且有效的解决方案。我们的代码和模型可在以下链接获取：[提供的URL]。
URL
https://arxiv.org/abs/2508.03064
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Lifelong Person Re-identification via Privacy-Preserving Data Replay
2025-08-03 05:00:19Mingyu Wang, Haojie Liu, Zhiyong Li, Wei JiangAbstract
Lifelong person re-identification (LReID) aims to incrementally accumulate knowledge across a sequence of tasks under domain shifts. Recently, replay-based methods have demonstrated strong effectiveness in LReID by rehearsing past samples stored in an auxiliary memory. However, storing historical exemplars raises concerns over data privacy. To avoid this, exemplar-free approaches attempt to match the distribution of past data without storing raw samples. Despite being privacy-friendly, these methods often suffer from performance degradation due to the forgetting of specific past knowledge representations. To this end, we propose to condense information from sequential data into the pixel space in the replay memory, enabling Privacy-Preserving Replay (Pr^2R). More specifically, by distilling the training characteristics of multiple real images into a single image, the condensed samples undergo pixel-level changes. This not only protects the privacy of the original data but also makes the replay samples more representative for sequential tasks. During the style replay phase, we align the current domain to the previous one while simultaneously adapting the replay samples to match the style of the current domain. This dual-alignment strategy effectively mitigates both class-incremental challenges and forgetting caused by domain shifts. Extensive experiments on multiple benchmarks show that the proposed method significantly improves replay effectiveness while preserving data privacy. Specifically, Pr^2R achieves 4% and 6% higher accuracy on sequential tasks compared to the current state-of-the-art and other replay-based methods, respectively.
Abstract (translated)
终身人员重识别（LReID）的目标是在领域变化的序列任务中逐步积累知识。最近，基于重放的方法通过在辅助记忆中存储过去的样本进行回放，在LReID中展示了很强的有效性。然而，存储历史示例引发了数据隐私方面的担忧。为了避免这种情况，无示例方法尝试匹配过去数据的分布而不存储原始样本。尽管这些方法对隐私友好，但由于忘记了特定的过去知识表示，它们通常会因性能下降而受到困扰。 为此，我们提出了一种将顺序数据的信息浓缩到重放记忆中的像素空间的方法，从而实现隐私保护型重放（Pr^2R）。具体而言，通过将多个真实图像的训练特征提炼成单个图像，浓缩后的样本在像素级别发生变化。这不仅保护了原始数据的隐私，还使重播样本更具有代表性以应对序列任务。在风格重放阶段，我们将当前领域与先前领域对齐的同时适应性地调整重放样本，使其符合当前领域的样式。这种双对齐策略有效地缓解了增量分类问题以及由领域变化引起的遗忘现象。 多项基准测试的广泛实验表明，所提出的方法显著提高了重放的有效性，同时保持数据隐私。具体而言，在序列任务上，Pr^2R相比目前最先进的方法和其它基于重播的方法分别取得了4%和6%更高的准确率。
URL
https://arxiv.org/abs/2508.01587
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Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification
2025-07-25 06:59:05Yuan Tian, Shuo Wang, Rongzhao Zhang, Zijian Chen, Yankai Jiang, Chunyi Li, Xiangyang Zhu, Fang Yan, Qiang Hu, XiaoSong Wang, Guangtao ZhaiAbstract
Medical imaging has significantly advanced computer-aided diagnosis, yet its re-identification (ReID) risks raise critical privacy concerns, calling for de-identification (DeID) techniques. Unfortunately, existing DeID methods neither particularly preserve medical semantics, nor are flexibly adjustable towards different privacy levels. To address these issues, we propose a divide-and-conquer framework comprising two steps: (1) Identity-Blocking, which blocks varying proportions of identity-related regions, to achieve different privacy levels; and (2) Medical-Semantics-Compensation, which leverages pre-trained Medical Foundation Models (MFMs) to extract medical semantic features to compensate the blocked regions. Moreover, recognizing that features from MFMs may still contain residual identity information, we introduce a Minimum Description Length principle-based feature decoupling strategy, to effectively decouple and discard such identity components. Extensive evaluations against existing approaches across seven datasets and three downstream tasks, demonstrates our state-of-the-art performance.
Abstract (translated)
医学影像技术在计算机辅助诊断方面取得了显著进展，然而其重新识别（ReID）风险引发了重要的隐私问题，因此需要采用脱敏（DeID）技术。不幸的是，现有的DeID方法既不能特别保留医疗语义信息，也不能灵活调整以适应不同的隐私级别需求。为了解决这些问题，我们提出了一种分而治之框架，包括两个步骤：(1) 身份屏蔽，通过遮蔽不同比例的身份相关区域来实现不同程度的隐私保护；(2) 医学语义补偿，利用预训练的医学基础模型（MFMs）提取医学语义特征以补充被屏蔽的区域。此外，考虑到来自MFM的特征可能仍包含残留的身份信息，我们引入了一种基于最小描述长度原理的特征解耦策略，有效分离并丢弃这些身份成分。在七组数据集和三种下游任务上的广泛评估显示了我们的方法处于业界领先水平。
URL
https://arxiv.org/abs/2507.21703
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Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs
2025-07-24 03:12:16Tevin Atwal, Chan Nam Tieu, Yefeng Yuan, Zhan Shi, Yuhong Liu, Liang ChengAbstract
The increasing use of synthetic data generated by Large Language Models (LLMs) presents both opportunities and challenges in data-driven applications. While synthetic data provides a cost-effective, scalable alternative to real-world data to facilitate model training, its diversity and privacy risks remain underexplored. Focusing on text-based synthetic data, we propose a comprehensive set of metrics to quantitatively assess the diversity (i.e., linguistic expression, sentiment, and user perspective), and privacy (i.e., re-identification risk and stylistic outliers) of synthetic datasets generated by several state-of-the-art LLMs. Experiment results reveal significant limitations in LLMs' capabilities in generating diverse and privacy-preserving synthetic data. Guided by the evaluation results, a prompt-based approach is proposed to enhance the diversity of synthetic reviews while preserving reviewer privacy.
Abstract (translated)
大型语言模型（LLM）生成的合成数据在数据驱动应用程序中既带来了机会也带来了挑战。尽管合成数据为现实世界数据提供了一种成本效益高且可扩展的替代方案，以促进模型训练，但其多样性和隐私风险仍然有待深入研究。专注于基于文本的合成数据，我们提出了一系列全面的指标来定量评估由几种最先进的LLM生成的数据集在多样性（即语言表达、情感和用户视角）和隐私（即再识别风险和风格异常值）方面的表现。实验结果揭示了LLM在生成多样性和保护隐私的合成数据方面存在显著局限性。根据评估结果，我们提出了一种基于提示的方法来增强合成评论的多样性，同时保持评论者的隐私。
URL
https://arxiv.org/abs/2507.18055
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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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PAT++: a cautionary tale about generative visual augmentation for Object Re-identification
2025-07-19 15:01:05Leonardo Santiago Benitez Pereira, Arathy JeevanAbstract
Generative data augmentation has demonstrated gains in several vision tasks, but its impact on object re-identification - where preserving fine-grained visual details is essential - remains largely unexplored. In this work, we assess the effectiveness of identity-preserving image generation for object re-identification. Our novel pipeline, named PAT++, incorporates Diffusion Self-Distillation into the well-established Part-Aware Transformer. Using the Urban Elements ReID Challenge dataset, we conduct extensive experiments with generated images used for both model training and query expansion. Our results show consistent performance degradation, driven by domain shifts and failure to retain identity-defining features. These findings challenge assumptions about the transferability of generative models to fine-grained recognition tasks and expose key limitations in current approaches to visual augmentation for identity-preserving applications.
Abstract (translated)
生成式数据增强在多个视觉任务中展示了增益，但在对象再识别（其中保留细微的视觉细节至关重要）方面的影响仍然很大程度上未被探索。在这项工作中，我们评估了身份保持型图像生成在对象再识别中的有效性。我们的新型流程，名为PAT++，将扩散自我蒸馏集成到了广受认可的部分感知变压器中。利用Urban Elements ReID Challenge数据集，我们在模型训练和查询扩展过程中使用生成的图像进行了广泛的实验。 我们的结果表明，在驱动领域变化的情况下，性能出现了持续下降，并且无法保留定义身份的关键特征。这些发现挑战了关于生成式模型在细粒度识别任务中的可迁移性的假设，并揭示了当前用于身份保持应用的视觉增强方法中的一些关键限制。
URL
https://arxiv.org/abs/2507.15888
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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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What cat is that? A re-id model for feral cats
2025-07-15 05:24:38Victor CaquilpanAbstract
Feral cats exert a substantial and detrimental impact on Australian wildlife, placing them among the most dangerous invasive species worldwide. Therefore, closely monitoring these cats is essential labour in minimising their effects. In this context, the potential application of Re-Identification (re-ID) emerges to enhance monitoring activities for these animals, utilising images captured by camera traps. This project explores different CV approaches to create a re-ID model able to identify individual feral cats in the wild. The main approach consists of modifying a part-pose guided network (PPGNet) model, initially used in the re-ID of Amur tigers, to be applicable for feral cats. This adaptation, resulting in PPGNet-Cat, which incorporates specific modifications to suit the characteristics of feral cats images. Additionally, various experiments were conducted, particularly exploring contrastive learning approaches such as ArcFace loss. The main results indicate that PPGNet-Cat excels in identifying feral cats, achieving high performance with a mean Average Precision (mAP) of 0.86 and a rank-1 accuracy of 0.95. These outcomes establish PPGNet-Cat as a competitive model within the realm of re-ID.
Abstract (translated)
野猫在澳大利亚对野生动物造成了严重的负面影响，使其成为世界上最危险的入侵物种之一。因此，在减少其影响方面，密切监测这些动物是至关重要的工作。在此背景下，重新识别（Re-Identification, Re-ID）技术的应用前景被用于增强针对这些动物的监控活动，利用相机陷阱捕捉到的照片进行操作。本项目探讨了不同的计算机视觉（CV）方法来创建一个能够识别野外个体野猫的身份重识别模型。主要的方法是修改最初用于阿穆尔虎身份重新识别的部分姿态引导网络（PPGNet）模型，使其适用于野猫的特征。这一改编产生了名为 PPGNet-Cat 的新版本，通过特定的修改来适应野猫照片的特点。此外，还进行了各种实验，尤其是探索对比学习方法如 ArcFace 损失函数的应用。 主要的结果表明，PPGNet-Cat 在识别野猫方面表现出色，达到了平均精度（mAP）为 0.86 和首名准确率（rank-1 accuracy）为 0.95 的高性能。这些结果确立了 PPGNet-Cat 在身份重识别领域中的竞争力地位。
URL
https://arxiv.org/abs/2507.11575
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