Re-identification
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Modality-Transition Representation Learning for Visible-Infrared Person Re-Identification
2025-11-04 16:09:28Chao Yuan, Zanwu Liu, Guiwei Zhang, Haoxuan Xu, Yujian Zhao, Guanglin Niu, Bo LiAbstract
Visible-infrared person re-identification (VI-ReID) technique could associate the pedestrian images across visible and infrared modalities in the practical scenarios of background illumination changes. However, a substantial gap inherently exists between these two modalities. Besides, existing methods primarily rely on intermediate representations to align cross-modal features of the same person. The intermediate feature representations are usually create by generating intermediate images (kind of data enhancement), or fusing intermediate features (more parameters, lack of interpretability), and they do not make good use of the intermediate features. Thus, we propose a novel VI-ReID framework via Modality-Transition Representation Learning (MTRL) with a middle generated image as a transmitter from visible to infrared modals, which are fully aligned with the original visible images and similar to the infrared modality. After that, using a modality-transition contrastive loss and a modality-query regularization loss for training, which could align the cross-modal features more effectively. Notably, our proposed framework does not need any additional parameters, which achieves the same inference speed to the backbone while improving its performance on VI-ReID task. Extensive experimental results illustrate that our model significantly and consistently outperforms existing SOTAs on three typical VI-ReID datasets.
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URL
https://arxiv.org/abs/2511.02685
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Seeing Across Time and Views: Multi-Temporal Cross-View Learning for Robust Video Person Re-Identification
2025-11-04 13:37:59Md Rashidunnabi, Kailash A. Hambarde, Vasco Lopes, Joao C. Neves, Hugo ProencaAbstract
Video-based person re-identification (ReID) in cross-view domains (for example, aerial-ground surveillance) remains an open problem because of extreme viewpoint shifts, scale disparities, and temporal inconsistencies. To address these challenges, we propose MTF-CVReID, a parameter-efficient framework that introduces seven complementary modules over a ViT-B/16 backbone. Specifically, we include: (1) Cross-Stream Feature Normalization (CSFN) to correct camera and view biases; (2) Multi-Resolution Feature Harmonization (MRFH) for scale stabilization across altitudes; (3) Identity-Aware Memory Module (IAMM) to reinforce persistent identity traits; (4) Temporal Dynamics Modeling (TDM) for motion-aware short-term temporal encoding; (5) Inter-View Feature Alignment (IVFA) for perspective-invariant representation alignment; (6) Hierarchical Temporal Pattern Learning (HTPL) to capture multi-scale temporal regularities; and (7) Multi-View Identity Consistency Learning (MVICL) that enforces cross-view identity coherence using a contrastive learning paradigm. Despite adding only about 2 million parameters and 0.7 GFLOPs over the baseline, MTF-CVReID maintains real-time efficiency (189 FPS) and achieves state-of-the-art performance on the AG-VPReID benchmark across all altitude levels, with strong cross-dataset generalization to G2A-VReID and MARS datasets. These results show that carefully designed adapter-based modules can substantially enhance cross-view robustness and temporal consistency without compromising computational efficiency. The source code is available at this https URL
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URL
https://arxiv.org/abs/2511.02564
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PCD-ReID: Occluded Person Re-Identification for Base Station Inspection
2025-11-03 13:07:19Ge Gao, Zishuo Gao, Hongyan Cui, Zhiyang Jia, Zhuang Luo, ChaoPeng LiuAbstract
Occluded pedestrian re-identification (ReID) in base station environments is a critical task in computer vision, particularly for surveillance and security applications. This task faces numerous challenges, as occlusions often obscure key body features, increasing the complexity of identification. Traditional ResNet-based ReID algorithms often fail to address occlusions effectively, necessitating new ReID methods. We propose the PCD-ReID (Pedestrian Component Discrepancy) algorithm to address these issues. The contributions of this work are as follows: To tackle the occlusion problem, we design a Transformer-based PCD network capable of extracting shared component features, such as helmets and uniforms. To mitigate overfitting on public datasets, we collected new real-world patrol surveillance images for model training, covering six months, 10,000 individuals, and over 50,000 images. Comparative experiments with existing ReID algorithms demonstrate that our model achieves a mean Average Precision (mAP) of 79.0% and a Rank-1 accuracy of 82.7%, marking a 15.9% Rank-1 improvement over ResNet50-based methods. Experimental evaluations indicate that PCD-ReID effectively achieves occlusion-aware ReID performance for personnel in tower inspection scenarios, highlighting its potential for practical deployment in surveillance and security applications.
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URL
https://arxiv.org/abs/2511.01546
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EPAN: Robust Pedestrian Re-Identification via Enhanced Alignment Network for IoT Surveillance
2025-11-03 12:04:32Zhiyang Jia, Hongyan Cui, Ge Gao, Bo Li, Minjie Zhang, Zishuo Gao, Huiwen Huang, Caisheng ZhuoAbstract
Person re-identification (ReID) plays a pivotal role in computer vision, particularly in surveillance and security applications within IoT-enabled smart environments. This study introduces the Enhanced Pedestrian Alignment Network (EPAN), tailored for robust ReID across diverse IoT surveillance conditions. EPAN employs a dual-branch architecture to mitigate the impact of perspective and environmental changes, extracting alignment information under varying scales and viewpoints. Here, we demonstrate EPAN's strong feature extraction capabilities, achieving outstanding performance on the Inspection-Personnel dataset with a Rank-1 accuracy of 90.09% and a mean Average Precision (mAP) of 78.82%. This highlights EPAN's potential for real-world IoT applications, enabling effective and reliable person ReID across diverse cameras in surveillance and security systems. The code and data are available at: this https URL
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URL
https://arxiv.org/abs/2511.01498
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Vision Transformer for Robust Occluded Person Reidentification in Complex Surveillance Scenes
2025-10-31 17:43:50Bo Li, Duyuan Zheng, Xinyang Liu, Qingwen Li, Hong Li, Hongyan Cui, Ge Gao, Chen LiuAbstract
Person re-identification (ReID) in surveillance is challenged by occlusion, viewpoint distortion, and poor image quality. Most existing methods rely on complex modules or perform well only on clear frontal images. We propose Sh-ViT (Shuffling Vision Transformer), a lightweight and robust model for occluded person ReID. Built on ViT-Base, Sh-ViT introduces three components: First, a Shuffle module in the final Transformer layer to break spatial correlations and enhance robustness to occlusion and blur; Second, scenario-adapted augmentation (geometric transforms, erasing, blur, and color adjustment) to simulate surveillance conditions; Third, DeiT-based knowledge distillation to improve learning with limited this http URL support real-world evaluation, we construct the MyTT dataset, containing over 10,000 pedestrians and 30,000+ images from base station inspections, with frequent equipment occlusion and camera variations. Experiments show that Sh-ViT achieves 83.2% Rank-1 and 80.1% mAP on MyTT, outperforming CNN and ViT baselines, and 94.6% Rank-1 and 87.5% mAP on Market1501, surpassing state-of-the-art this http URL summary, Sh-ViT improves robustness to occlusion and blur without external modules, offering a practical solution for surveillance-based personnel monitoring.
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URL
https://arxiv.org/abs/2510.27677
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MDReID: Modality-Decoupled Learning for Any-to-Any Multi-Modal Object Re-Identification
2025-10-27 13:08:46Yingying Feng, Jie Li, Jie Hu, Yukang Zhang, Lei Tan, Jiayi JiAbstract
Real-world object re-identification (ReID) systems often face modality inconsistencies, where query and gallery images come from different sensors (e.g., RGB, NIR, TIR). However, most existing methods assume modality-matched conditions, which limits their robustness and scalability in practical applications. To address this challenge, we propose MDReID, a flexible any-to-any image-level ReID framework designed to operate under both modality-matched and modality-mismatched scenarios. MDReID builds on the insight that modality information can be decomposed into two components: modality-shared features that are predictable and transferable, and modality-specific features that capture unique, modality-dependent characteristics. To effectively leverage this, MDReID introduces two key components: the Modality Decoupling Learning (MDL) and Modality-aware Metric Learning (MML). Specifically, MDL explicitly decomposes modality features into modality-shared and modality-specific representations, enabling effective retrieval in both modality-aligned and mismatched scenarios. MML, a tailored metric learning strategy, further enforces orthogonality and complementarity between the two components to enhance discriminative power across modalities. Extensive experiments conducted on three challenging multi-modality ReID benchmarks (RGBNT201, RGBNT100, MSVR310) consistently demonstrate the superiority of MDReID. Notably, MDReID achieves significant mAP improvements of 9.8\%, 3.0\%, and 11.5\% in general modality-matched scenarios, and average gains of 3.4\%, 11.8\%, and 10.9\% in modality-mismatched scenarios, respectively. The code is available at: \textcolor{magenta}{this https URL}.
Abstract (translated)
URL
https://arxiv.org/abs/2510.23301
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GSAlign: Geometric and Semantic Alignment Network for Aerial-Ground Person Re-Identification
2025-10-25 12:16:10Qiao Li, Jie Li, Yukang Zhang, Lei Tan, Jing Chen, Jiayi JiAbstract
Aerial-Ground person re-identification (AG-ReID) is an emerging yet challenging task that aims to match pedestrian images captured from drastically different viewpoints, typically from unmanned aerial vehicles (UAVs) and ground-based surveillance cameras. The task poses significant challenges due to extreme viewpoint discrepancies, occlusions, and domain gaps between aerial and ground imagery. While prior works have made progress by learning cross-view representations, they remain limited in handling severe pose variations and spatial misalignment. To address these issues, we propose a Geometric and Semantic Alignment Network (GSAlign) tailored for AG-ReID. GSAlign introduces two key components to jointly tackle geometric distortion and semantic misalignment in aerial-ground matching: a Learnable Thin Plate Spline (LTPS) Module and a Dynamic Alignment Module (DAM). The LTPS module adaptively warps pedestrian features based on a set of learned keypoints, effectively compensating for geometric variations caused by extreme viewpoint changes. In parallel, the DAM estimates visibility-aware representation masks that highlight visible body regions at the semantic level, thereby alleviating the negative impact of occlusions and partial observations in cross-view correspondence. A comprehensive evaluation on CARGO with four matching protocols demonstrates the effectiveness of GSAlign, achieving significant improvements of +18.8\% in mAP and +16.8\% in Rank-1 accuracy over previous state-of-the-art methods on the aerial-ground setting. The code is available at: \textcolor{magenta}{this https URL}.
Abstract (translated)
URL
https://arxiv.org/abs/2510.22268
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FutrTrack: A Camera-LiDAR Fusion Transformer for 3D Multiple Object Tracking
2025-10-22 19:25:01Martha Teiko Teye, Ori Maoz, Matthias RottmannAbstract
We propose FutrTrack, a modular camera-LiDAR multi-object tracking framework that builds on existing 3D detectors by introducing a transformer-based smoother and a fusion-driven tracker. Inspired by query-based tracking frameworks, FutrTrack employs a multimodal two-stage transformer refinement and tracking pipeline. Our fusion tracker integrates bounding boxes with multimodal bird's-eye-view (BEV) fusion features from multiple cameras and LiDAR without the need for an explicit motion model. The tracker assigns and propagates identities across frames, leveraging both geometric and semantic cues for robust re-identification under occlusion and viewpoint changes. Prior to tracking, we refine sequences of bounding boxes with a temporal smoother over a moving window to refine trajectories, reduce jitter, and improve spatial consistency. Evaluated on nuScenes and KITTI, FutrTrack demonstrates that query-based transformer tracking methods benefit significantly from multimodal sensor features compared with previous single-sensor approaches. With an aMOTA of 74.7 on the nuScenes test set, FutrTrack achieves strong performance on 3D MOT benchmarks, reducing identity switches while maintaining competitive accuracy. Our approach provides an efficient framework for improving transformer-based trackers to compete with other neural-network-based methods even with limited data and without pretraining.
Abstract (translated)
我们提出了一种名为FutrTrack的模块化相机-激光雷达多目标跟踪框架，该框架在现有的3D检测器基础上引入了基于变换器的平滑器和融合驱动的跟踪器。受查询基元跟踪框架的启发，FutrTrack采用了一个跨模态两阶段变换器细化和跟踪流水线。我们的融合跟踪器将边界框与来自多个相机和激光雷达的多模态鸟瞰图（BEV）融合特征相结合，并不需要明确的运动模型。该跟踪器可以在帧之间分配并传播身份信息，利用几何和语义线索，在遮挡和视角变化的情况下实现稳健的重新识别。在进行追踪之前，我们通过在一个移动窗口上使用时序平滑器来细化边界框序列，以优化轨迹、减少抖动，并提高空间一致性。 在nuScenes和KITTI数据集上的评估表明，查询基元变换器跟踪方法从多模态传感器特征中受益匪浅，相较于之前的单传感器方法。FutrTrack在nuScenes测试集中达到了74.7的aMOTA（平均多目标跟踪准确率），展示了其在3D MOT基准测试中的强劲性能，在减少身份切换的同时保持了竞争力的精度。 我们的方法提供了一个高效的框架，用于改进基于变换器的追踪器，使其能够在数据有限且无预训练的情况下与其它神经网络方法竞争。
URL
https://arxiv.org/abs/2510.19981
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Person Re-Identification via Generalized Class Prototypes
2025-10-19 23:16:57Md Ahmed Al Muzaddid, William J. BeksiAbstract
Advanced feature extraction methods have significantly contributed to enhancing the task of person re-identification. In addition, modifications to objective functions have been developed to further improve performance. Nonetheless, selecting better class representatives is an underexplored area of research that can also lead to advancements in re-identification performance. Although past works have experimented with using the centroid of a gallery image class during training, only a few have investigated alternative representations during the retrieval stage. In this paper, we demonstrate that these prior techniques yield suboptimal results in terms of re-identification metrics. To address the re-identification problem, we propose a generalized selection method that involves choosing representations that are not limited to class centroids. Our approach strikes a balance between accuracy and mean average precision, leading to improvements beyond the state of the art. For example, the actual number of representations per class can be adjusted to meet specific application requirements. We apply our methodology on top of multiple re-identification embeddings, and in all cases it substantially improves upon contemporary results
Abstract (translated)
先进的特征提取方法在提升人员重识别任务性能方面发挥了重要作用。此外，针对目标函数的改进也进一步提升了性能表现。然而，选择更好的类别代表是一个尚未充分研究的研究领域，这一领域的探索也可以促进重识别性能的进步。虽然过去的工作中曾尝试在训练阶段使用图像类别的质心作为代表性样本，但在检索阶段探索其他替代表示形式的研究却很少。本文展示了先前的技术手段在人员重识别指标方面表现欠佳。 为了解决重识别问题，我们提出了一种通用的选择方法，该方法不局限于类别质心的选取，而是选择更加多样化的代表以优化性能。我们的方法在准确性和平均精度之间取得了平衡，并超越了现有的技术水平。例如，可以根据具体的应用需求调整每个类别的代表性样本数量。 我们将所提的方法应用于多种重识别嵌入上，在所有情况下均显著提升了当前的最佳结果。
URL
https://arxiv.org/abs/2510.17043
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FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification
2025-10-17 12:41:05Zhen Sun, Lei Tan, Yunhang Shen, Chengmao Cai, Xing Sun, Pingyang Dai, Liujuan Cao, Rongrong JiAbstract
Multimodal person re-identification (Re-ID) aims to match pedestrian images across different modalities. However, most existing methods focus on limited cross-modal settings and fail to support arbitrary query-retrieval combinations, hindering practical deployment. We propose FlexiReID, a flexible framework that supports seven retrieval modes across four modalities: rgb, infrared, sketches, and text. FlexiReID introduces an adaptive mixture-of-experts (MoE) mechanism to dynamically integrate diverse modality features and a cross-modal query fusion module to enhance multimodal feature extraction. To facilitate comprehensive evaluation, we construct CIRS-PEDES, a unified dataset extending four popular Re-ID datasets to include all four modalities. Extensive experiments demonstrate that FlexiReID achieves state-of-the-art performance and offers strong generalization in complex scenarios.
Abstract (translated)
多模态行人重识别（Re-ID）旨在匹配不同模态下的行人图像。然而，大多数现有方法主要关注有限的跨模态设置，并且无法支持任意查询-检索组合，这阻碍了其实用部署。我们提出了FlexiReID，这是一个灵活框架，支持四种模式（RGB、红外、素描和文本）之间的七种检索模式。FlexiReID引入了一种自适应专家混合（MoE）机制，以动态集成不同模态的特征，并设计了一个跨模态查询融合模块来增强多模态特征提取。为了促进全面评估，我们构建了CIRS-PEDES数据集，这是一个统一的数据集，扩展了四个流行的Re-ID数据集，包含了所有四种模式。广泛的实验表明，FlexiReID在复杂场景中达到了最先进的性能，并且具有强大的泛化能力。
URL
https://arxiv.org/abs/2510.15595
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CALM-Net: Curvature-Aware LiDAR Point Cloud-based Multi-Branch Neural Network for Vehicle Re-Identification
2025-10-16 11:36:54Dongwook Lee, Sol Han, Jinwhan KimAbstract
This paper presents CALM-Net, a curvature-aware LiDAR point cloud-based multi-branch neural network for vehicle re-identification. The proposed model addresses the challenge of learning discriminative and complementary features from three-dimensional point clouds to distinguish between vehicles. CALM-Net employs a multi-branch architecture that integrates edge convolution, point attention, and a curvature embedding that characterizes local surface variation in point clouds. By combining these mechanisms, the model learns richer geometric and contextual features that are well suited for the re-identification task. Experimental evaluation on the large-scale nuScenes dataset demonstrates that CALM-Net achieves a mean re-identification accuracy improvement of approximately 1.97\% points compared with the strongest baseline in our study. The results confirms the effectiveness of incorporating curvature information into deep learning architectures and highlight the benefit of multi-branch feature learning for LiDAR point cloud-based vehicle re-identification.
Abstract (translated)
本文介绍了CALM-Net，这是一种基于曲率感知的LiDAR点云的多分支神经网络，用于车辆再识别。所提出的模型旨在解决从三维点云中学习区分性和互补性特征以区分不同车辆的挑战。CALM-Net采用了一种多分支架构，集成了边缘卷积、点注意力和一种能够表征点云局部表面变化的曲率嵌入。通过结合这些机制，该模型可以学习到更丰富的几何和上下文特征，非常适合于再识别任务。在大规模nuScenes数据集上的实验评估表明，CALM-Net相较于我们在研究中选取的最佳基线模型，在车辆再识别精度上提升了约1.97个百分点的平均值。结果证实了将曲率信息融入深度学习架构的有效性，并突出了多分支特征学习在基于LiDAR点云的车辆再识别中的优势。
URL
https://arxiv.org/abs/2510.14576
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Fast Self-Supervised depth and mask aware Association for Multi-Object Tracking
2025-10-10 21:32:24Milad Khanchi, Maria Amer, Charalambos PoullisAbstract
Multi-object tracking (MOT) methods often rely on Intersection-over-Union (IoU) for association. However, this becomes unreliable when objects are similar or occluded. Also, computing IoU for segmentation masks is computationally expensive. In this work, we use segmentation masks to capture object shapes, but we do not compute segmentation IoU. Instead, we fuse depth and mask features and pass them through a compact encoder trained self-supervised. This encoder produces stable object representations, which we use as an additional similarity cue alongside bounding box IoU and re-identification features for matching. We obtain depth maps from a zero-shot depth estimator and object masks from a promptable visual segmentation model to obtain fine-grained spatial cues. Our MOT method is the first to use the self-supervised encoder to refine segmentation masks without computing masks IoU. MOT can be divided into joint detection-ReID (JDR) and tracking-by-detection (TBD) models. The latter are computationally more efficient. Experiments of our TBD method on challenging benchmarks with non-linear motion, occlusion, and crowded scenes, such as SportsMOT and DanceTrack, show that our method outperforms the TBD state-of-the-art on most metrics, while achieving competitive performance on simpler benchmarks with linear motion, such as MOT17.
Abstract (translated)
多目标跟踪（MOT）方法通常依赖于交并比（IoU）进行关联。然而，当对象相似或被遮挡时，这种方法变得不可靠。此外，计算分割掩码的IoU在计算上是昂贵的。在这项工作中，我们使用分割掩码来捕捉物体形状，但不计算分割IoU。相反，我们将深度和掩码特征融合，并通过一种经过自监督训练的小型编码器进行处理。这种编码器产生稳定的目标表示，我们在匹配时将其作为边界框IoU和重新识别特征之外的额外相似性线索使用。我们从零样本深度估计器获得深度图，并从可提示视觉分割模型中获取物体掩码，以获得细粒度的空间线索。 我们的MOT方法是第一个利用自监督编码器来改进分割掩码而不计算掩码IoU的方法。MOT可以分为联合检测-重识别（JDR）和基于检测的跟踪（TBD）模型两大类。后者在计算效率上更为高效。我们在具有非线性运动、遮挡以及拥挤场景等挑战性的基准测试，如SportsMOT和DanceTrack上的实验表明，我们的方法在大多数指标上优于现有的TBD最佳方法，并且在包含线性运动的简单基准测试（例如MOT17）中也取得了竞争性表现。
URL
https://arxiv.org/abs/2510.09878
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Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey
2025-10-10 11:45:01Muhammad Munsif, Waqas Ahmad, Amjid Ali, Mohib Ullah, Adnan Hussain, Sung Wook BaikAbstract
Connected Vision Systems (CVS) are transforming a variety of applications, including autonomous vehicles, smart cities, surveillance, and human-robot interaction. These systems harness multi-view multi-camera (MVMC) data to provide enhanced situational awareness through the integration of MVMC tracking, re-identification (Re-ID), and action understanding (AU). However, deploying CVS in real-world, dynamic environments presents a number of challenges, particularly in addressing occlusions, diverse viewpoints, and environmental variability. Existing surveys have focused primarily on isolated tasks such as tracking, Re-ID, and AU, often neglecting their integration into a cohesive system. These reviews typically emphasize single-view setups, overlooking the complexities and opportunities provided by multi-camera collaboration and multi-view data analysis. To the best of our knowledge, this survey is the first to offer a comprehensive and integrated review of MVMC that unifies MVMC tracking, Re-ID, and AU into a single framework. We propose a unique taxonomy to better understand the critical components of CVS, dividing it into four key parts: MVMC tracking, Re-ID, AU, and combined methods. We systematically arrange and summarize the state-of-the-art datasets, methodologies, results, and evaluation metrics, providing a structured view of the field's progression. Furthermore, we identify and discuss the open research questions and challenges, along with emerging technologies such as lifelong learning, privacy, and federated learning, that need to be addressed for future advancements. The paper concludes by outlining key research directions for enhancing the robustness, efficiency, and adaptability of CVS in complex, real-world applications. We hope this survey will inspire innovative solutions and guide future research toward the next generation of intelligent and adaptive CVS.
Abstract (translated)
连接视觉系统（CVS）正在改变从自动驾驶汽车、智慧城市监控到人机交互等多种应用场景。这些系统利用多视点多摄像头(MVMC)数据，通过整合MVMC跟踪、重识别(Re-ID)和行为理解(AU)，提供了增强的情景感知能力。然而，在现实世界的动态环境中部署CVS面临着多种挑战，尤其是处理遮挡、多样视角以及环境变化等问题。现有的调查研究大多集中在诸如追踪、Re-ID和AU等孤立任务上，并往往忽略了将这些技术集成到一个统一系统中的问题。此外，这些审查通常侧重于单视图设置，忽视了多摄像头协作与多视数据分析带来的复杂性和机遇。 据我们所知，本综述是首次提供了一个全面而整合的MVMC评估框架，该框架将MVMC跟踪、Re-ID和AU统一在一个单一的体系结构中。我们提出了一种独特的分类方法来更好地理解CVS的关键组成部分，并将其分为四个主要部分：MVMC跟踪、Re-ID、AU及综合方法。我们系统地整理并总结了当前最先进的数据集、方法论、结果以及评估指标，为该领域的进展提供了一个有条理的视角。 此外，本综述还识别和讨论了开放的研究问题与挑战，并提及了诸如终身学习、隐私保护和联邦学习等新兴技术，在未来的发展中需要解决这些问题。文章最后提出了增强CVS在复杂现实应用中的鲁棒性、效率和适应性的关键研究方向。我们希望这项调查能够激发创新的解决方案，指导未来的研发向着智能且自适应的下一代连接视觉系统迈进。
URL
https://arxiv.org/abs/2510.09731
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Stronger Re-identification Attacks through Reasoning and Aggregation
2025-10-10 09:27:42Lucas Georges Gabriel Charpentier, Pierre LisonAbstract
Text de-identification techniques are often used to mask personally identifiable information (PII) from documents. Their ability to conceal the identity of the individuals mentioned in a text is, however, hard to measure. Recent work has shown how the robustness of de-identification methods could be assessed by attempting the reverse process of _re-identification_, based on an automated adversary using its background knowledge to uncover the PIIs that have been masked. This paper presents two complementary strategies to build stronger re-identification attacks. We first show that (1) the _order_ in which the PII spans are re-identified matters, and that aggregating predictions across multiple orderings leads to improved results. We also find that (2) reasoning models can boost the re-identification performance, especially when the adversary is assumed to have access to extensive background knowledge.
Abstract (translated)
文本去识别技术通常用于从文档中屏蔽个人身份信息（PII）。然而，衡量这些技术隐藏文本中提及的个体身份的能力是困难的。最近的研究表明，可以通过尝试逆向过程——即“再识别”来评估去识别方法的稳健性。这种再识别基于自动化对手使用其背景知识来揭示已被屏蔽的PII。本文提出了两种互补策略以构建更强的再识别攻击。首先，我们展示了（1）在重新识别PII跨度时顺序的重要性，并且跨多个顺序聚合预测可以提高结果。此外，我们还发现（2）推理模型能够提升再识别性能，尤其是在假设对手具有大量背景知识的情况下更是如此。
URL
https://arxiv.org/abs/2510.09184
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Re-Identifying K={a}k={a} with AI-Automated Video Key Frame Extraction
2025-10-09 19:46:46Paula Maddigan, Andrew Lensen, Rachael C. ShawAbstract
Accurate recognition and re-identification of individual animals is essential for successful wildlife population monitoring. Traditional methods, such as leg banding of birds, are time consuming and invasive. Recent progress in artificial intelligence, particularly computer vision, offers encouraging solutions for smart conservation and efficient automation. This study presents a unique pipeline for extracting high-quality key frames from videos of kÄkÄ (Nestor meridionalis), a threatened forest-dwelling parrot in New Zealand. Key frame extraction is well-studied in person re-identification, however, its application to wildlife is limited. Using video recordings at a custom-built feeder, we extract key frames and evaluate the re-identification performance of our pipeline. Our unsupervised methodology combines object detection using YOLO and Grounding DINO, optical flow blur detection, image encoding with DINOv2, and clustering methods to identify representative key frames. The results indicate that our proposed key frame selection methods yield image collections which achieve high accuracy in kÄkÄ re-identification, providing a foundation for future research using media collected in more diverse and challenging environments. Through the use of artificial intelligence and computer vision, our non-invasive and efficient approach provides a valuable alternative to traditional physical tagging methods for recognising kÄkÄ individuals and therefore improving the monitoring of populations. This research contributes to developing fresh approaches in wildlife monitoring, with applications in ecology and conservation biology.
Abstract (translated)
准确识别和重新识别野生动物个体对于成功监测野生动植物种群至关重要。传统方法，例如给鸟类佩戴脚环，既耗时又具有侵扰性。近年来，人工智能（特别是计算机视觉）的进步为智能保护和高效自动化提供了令人鼓舞的解决方案。本研究提出了一套独特的管道系统，用于从新西兰特有濒危森林鹦鹉——kākā (Nestor meridionalis) 的视频中提取高质量的关键帧。关键帧提取在人员重识别领域已得到充分研究，但在野生动物应用中的使用还相对有限。 我们通过定制设计的喂食器记录下的视频来提取这些关键帧，并评估该管道系统的重新识别性能。我们的无监督方法结合了YOLO和Grounding DINO进行物体检测、基于光流模糊检测的方法以及利用DINOv2进行图像编码，最后采用聚类技术以确定代表性的关键帧。 实验结果显示，我们提出的选帧方法能够生成高精度的用于kākā重新识别的图像集，在更多样化且更具挑战性的环境中采集媒体的基础上奠定了未来研究的基础。通过使用人工智能和计算机视觉，我们的非侵入性和高效的方法为传统的物理标记方法提供了一个有价值的替代方案，以更有效地识别个体 kākā 并改进种群监测。 这项研究有助于开发新的野生动物监测方法，在生态学和保护生物学领域具有广泛的应用价值。
URL
https://arxiv.org/abs/2510.08775
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Emergent AI Surveillance: Overlearned Person Re-Identification and Its Mitigation in Law Enforcement Context
2025-10-07 15:23:16An Thi Nguyen, Radina Stoykova, Eric ArazoAbstract
Generic instance search models can dramatically reduce the manual effort required to analyze vast surveillance footage during criminal investigations by retrieving specific objects of interest to law enforcement. However, our research reveals an unintended emergent capability: through overlearning, these models can single out specific individuals even when trained on datasets without human subjects. This capability raises concerns regarding identification and profiling of individuals based on their personal data, while there is currently no clear standard on how de-identification can be achieved. We evaluate two technical safeguards to curtail a model's person re-identification capacity: index exclusion and confusion loss. Our experiments demonstrate that combining these approaches can reduce person re-identification accuracy to below 2% while maintaining 82% of retrieval performance for non-person objects. However, we identify critical vulnerabilities in these mitigations, including potential circumvention using partial person images. These findings highlight urgent regulatory questions at the intersection of AI governance and data protection: How should we classify and regulate systems with emergent identification capabilities? And what technical standards should be required to prevent identification capabilities from developing in seemingly benign applications?
Abstract (translated)
通用实例搜索模型可以通过检索执法机关感兴趣的特定对象，显著减少分析大规模监控视频期间的手动工作量。然而，我们的研究揭示了一个未预见的意外能力：通过过度学习，即使在没有人类主体的数据集上进行训练，这些模型也能识别出特定个体。这种能力引发了关于基于个人数据对个体进行身份识别和画像的风险问题，而目前尚无明确的标准来规定如何实现去标识化。我们评估了两种技术保障措施以限制模型的人脸重新识别能力：索引排除和混淆损失。实验表明，结合这两种方法可以将人脸重新识别的准确性降低到2%以下，同时保持对非人物体检索性能的82%。 然而，我们也发现了这些缓解措施的关键漏洞，包括使用部分人体图像进行潜在规避的可能性。这些发现突显了在AI治理与数据保护交叉领域的紧急监管问题：我们应该如何分类和规范具备突发识别能力的系统？以及为了防止看似无害的应用程序中发展出身份识别能力，应该要求哪些技术标准？
URL
https://arxiv.org/abs/2510.06026
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RareGraph-Synth: Knowledge-Guided Diffusion Models for Generating Privacy-Preserving Synthetic Patient Trajectories in Ultra-Rare Diseases
2025-10-06 03:59:09Khartik Uppalapati, Shakeel Abdulkareem, Bora YimeniciogluAbstract
We propose RareGraph-Synth, a knowledge-guided, continuous-time diffusion framework that generates realistic yet privacy-preserving synthetic electronic-health-record (EHR) trajectories for ultra-rare diseases. RareGraph-Synth unifies five public resources: Orphanet/Orphadata, the Human Phenotype Ontology (HPO), the GARD rare-disease KG, PrimeKG, and the FDA Adverse Event Reporting System (FAERS) into a heterogeneous knowledge graph comprising approximately 8 M typed edges. Meta-path scores extracted from this 8-million-edge KG modulate the per-token noise schedule in the forward stochastic differential equation, steering generation toward biologically plausible lab-medication-adverse-event co-occurrences while retaining score-based diffusion model stability. The reverse denoiser then produces timestamped sequences of lab-code, medication-code, and adverse-event-flag triples that contain no protected health information. On simulated ultra-rare-disease cohorts, RareGraph-Synth lowers categorical Maximum Mean Discrepancy by 40 percent relative to an unguided diffusion baseline and by greater than 60 percent versus GAN counterparts, without sacrificing downstream predictive utility. A black-box membership-inference evaluation using the DOMIAS attacker yields AUROC approximately 0.53, well below the 0.55 safe-release threshold and substantially better than the approximately 0.61 plus or minus 0.03 observed for non-KG baselines, demonstrating strong resistance to re-identification. These results suggest that integrating biomedical knowledge graphs directly into diffusion noise schedules can simultaneously enhance fidelity and privacy, enabling safer data sharing for rare-disease research.
Abstract (translated)
我们提出了一种名为RareGraph-Synth的框架，这是一种知识引导型、连续时间扩散模型，用于生成现实且保护隐私的人工电子健康记录（EHR）轨迹，以支持罕见病研究。RareGraph-Synth将五个公开资源——Orphanet/Orphadata、人类表型本体论（HPO）、GARD罕见疾病知识图谱（KG）、PrimeKG以及美国食品和药物管理局不良事件报告系统（FAERS），整合成一个异构的知识图，包含大约800万条类型化的边。从这个800万边的KG中提取元路径得分来调节前向随机微分方程中的每令牌噪声时间表，在生成过程中引导生物学上合理的实验室-药物-不良事件共现模式的同时保持评分扩散模型的稳定性。随后，反向去噪器将产生包含实验室代码、药物代码和不良事件标志三元组的时间戳序列，并确保不泄露任何受保护的健康信息。 在模拟罕见病队列中，与未经引导的扩散基准相比，RareGraph-Synth将类别的最大均值差异降低了40%，比GAN同类模型的降低幅度更是超过了60%，而不会牺牲下游预测效用。使用DOMIAS攻击者进行黑盒成员推理评估时，AUROC大约为0.53，远低于安全发布的阈值0.55，并且明显优于非KG基线中观察到的大约0.61±0.03，证明了对重新识别的强大抵抗力。 这些结果表明，直接将生物医学知识图集成到扩散噪声时间表中可以同时提高数据的真实性和隐私保护水平，从而为罕见病研究提供更安全的数据共享环境。
URL
https://arxiv.org/abs/2510.06267
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Leveraging Prior Knowledge of Diffusion Model for Person Search
2025-10-02 09:36:26Giyeol Kim, Sooyoung Yang, Jihyong Oh, Myungjoo Kang, Chanho EomAbstract
Person search aims to jointly perform person detection and re-identification by localizing and identifying a query person within a gallery of uncropped scene images. Existing methods predominantly utilize ImageNet pre-trained backbones, which may be suboptimal for capturing the complex spatial context and fine-grained identity cues necessary for person search. Moreover, they rely on a shared backbone feature for both person detection and re-identification, leading to suboptimal features due to conflicting optimization objectives. In this paper, we propose DiffPS (Diffusion Prior Knowledge for Person Search), a novel framework that leverages a pre-trained diffusion model while eliminating the optimization conflict between two sub-tasks. We analyze key properties of diffusion priors and propose three specialized modules: (i) Diffusion-Guided Region Proposal Network (DGRPN) for enhanced person localization, (ii) Multi-Scale Frequency Refinement Network (MSFRN) to mitigate shape bias, and (iii) Semantic-Adaptive Feature Aggregation Network (SFAN) to leverage text-aligned diffusion features. DiffPS sets a new state-of-the-art on CUHK-SYSU and PRW.
Abstract (translated)
人搜索的目标是在一组未裁剪的场景图像中定位和识别查询人物，从而同时执行人员检测和重识别。现有方法主要使用基于ImageNet预训练的基础模型，这可能对于捕捉复杂的空间上下文和细微的身份线索以进行人员搜索而言不是最佳选择。此外，它们依赖于一个共享的基础特征用于人员检测和重识别任务，导致由于两个子任务的优化目标相互冲突而导致次优特征。 在本文中，我们提出了一种新的框架DiffPS（用于人员搜索的扩散先验知识），该框架利用预训练的扩散模型同时消除两个子任务之间的优化冲突。我们分析了扩散先验的关键属性，并提出了三个专门模块：(i) 弥漫引导区域提案网络 (DGRPN)，用于增强人物定位；(ii) 多尺度频率细化网络（MSFRN），以减轻形状偏差；以及(iii) 语义自适应特征聚合网络(SFAN)，利用与文本对齐的扩散特性。DiffPS在CUHK-SYSU和PRW数据集上达到了新的最先进水平。
URL
https://arxiv.org/abs/2510.01841
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Looking Alike From Far to Near: Enhancing Cross-Resolution Re-Identification via Feature Vector Panning
2025-10-01 14:15:39Zanwu Liu, Chao Yuan, Bo Li, Xiaowei Zhang, Guanglin NiuAbstract
In surveillance scenarios, varying camera distances cause significant differences among pedestrian image resolutions, making it hard to match low-resolution (LR) images with high-resolution (HR) counterparts, limiting the performance of Re-Identification (ReID) tasks. Most existing Cross-Resolution ReID (CR-ReID) methods rely on super-resolution (SR) or joint learning for feature compensation, which increases training and inference complexity and has reached a performance bottleneck in recent studies. Inspired by semantic directions in the word embedding space, we empirically discover that semantic directions implying resolution differences also emerge in the feature space of ReID, and we substantiate this finding from a statistical perspective using Canonical Correlation Analysis and Pearson Correlation Analysis. Based on this interesting finding, we propose a lightweight and effective Vector Panning Feature Alignment (VPFA) framework, which conducts CR-ReID from a novel perspective of modeling the resolution-specific feature discrepancy. Extensive experimental results on multiple CR-ReID benchmarks show that our method significantly outperforms previous state-of-the-art baseline models while obtaining higher efficiency, demonstrating the effectiveness and superiority of our model based on the new finding in this paper.
Abstract (translated)
在监控场景中，不同的摄像机距离导致行人图像分辨率存在显著差异，使得低分辨率（LR）图像与高分辨率（HR）图像的匹配变得困难，从而限制了重识别（ReID）任务的表现。大多数现有的跨分辨率重识别（CR-ReID）方法依赖于超分辨率（SR）或联合学习来进行特征补偿，这增加了训练和推理的复杂性，并且在最近的研究中已达到性能瓶颈。 受到词嵌入空间中语义方向的启发，我们通过实验发现，在ReID的特征空间中也存在表示分辨率差异的语义方向。利用典型相关分析（CCA）和皮尔逊相关分析从统计学角度证实了这一发现。基于这一有趣的发现，我们提出了一种轻量级且有效的向量平移特征对齐（VPFA）框架，该框架以一种新颖的角度来建模分辨率特异性的特征差异，并以此来进行跨分辨率重识别。 在多个CR-ReID基准测试上的广泛实验结果表明，我们的方法显著优于之前的最先进基线模型，并获得了更高的效率，证明了基于本文新发现的模型的有效性和优越性。
URL
https://arxiv.org/abs/2510.00936
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What Makes You Unique? Attribute Prompt Composition for Object Re-Identification
2025-09-23 07:03:08Yingquan Wang, Pingping Zhang, Chong Sun, Dong Wang, Huchuan LuAbstract
Object Re-IDentification (ReID) aims to recognize individuals across non-overlapping camera views. While recent advances have achieved remarkable progress, most existing models are constrained to either single-domain or cross-domain scenarios, limiting their real-world applicability. Single-domain models tend to overfit to domain-specific features, whereas cross-domain models often rely on diverse normalization strategies that may inadvertently suppress identity-specific discriminative cues. To address these limitations, we propose an Attribute Prompt Composition (APC) framework, which exploits textual semantics to jointly enhance discrimination and generalization. Specifically, we design an Attribute Prompt Generator (APG) consisting of a Semantic Attribute Dictionary (SAD) and a Prompt Composition Module (PCM). SAD is an over-complete attribute dictionary to provide rich semantic descriptions, while PCM adaptively composes relevant attributes from SAD to generate discriminative attribute-aware features. In addition, motivated by the strong generalization ability of Vision-Language Models (VLM), we propose a Fast-Slow Training Strategy (FSTS) to balance ReID-specific discrimination and generalizable representation learning. Specifically, FSTS adopts a Fast Update Stream (FUS) to rapidly acquire ReID-specific discriminative knowledge and a Slow Update Stream (SUS) to retain the generalizable knowledge inherited from the pre-trained VLM. Through a mutual interaction, the framework effectively focuses on ReID-relevant features while mitigating overfitting. Extensive experiments on both conventional and Domain Generalized (DG) ReID datasets demonstrate that our framework surpasses state-of-the-art methods, exhibiting superior performances in terms of both discrimination and generalization. The source code is available at this https URL.
Abstract (translated)
对象重识别（Re-ID）旨在通过非重叠的摄像头视角来识别个人。尽管最近的研究取得了显著进展，但大多数现有的模型要么局限于单领域场景，要么局限于跨领域场景，这限制了它们在现实世界中的应用能力。单领域的模型往往过度拟合于特定域的特征，而跨领域的模型则常常依赖多样化的归一化策略，这些策略可能无意中抑制了身份特异性的鉴别性线索。 为了克服这些局限性，我们提出了一种属性提示合成（APC）框架，该框架利用文本语义共同增强区分度和泛化能力。具体而言，我们设计了一个属性提示生成器（APG），它包括一个语义属性词典（SAD）和一个提示合成模块（PCM）。SAD是一个过度完备的属性字典，可以提供丰富的语义描述，而PCM则从SAD中自适应地组合相关属性以生成具有鉴别性的、感知身份特征。此外，鉴于视觉语言模型(VLM)强大的泛化能力，我们提出了一种快速-慢速训练策略（FSTS），旨在平衡Re-ID特异的区分度和通用表示学习。具体而言，FSTS采用了一个快速更新流（FUS）来迅速获取Re-ID特定的鉴别性知识，以及一个慢速更新流（SUS）来保留预训练VLM继承的一般化知识。通过相互作用，该框架可以有效关注与Re-ID相关的特征，同时减轻过拟合现象。 在传统和领域泛化的Re-ID数据集上的广泛实验表明，我们的框架超越了现有的最先进方法，在区分度和泛化性能方面表现出卓越的效能。源代码可在提供的链接中获取（原文中的链接）。
URL
https://arxiv.org/abs/2509.18715
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