Person_Re-identification
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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.
Abstract (translated)
URL
https://arxiv.org/abs/2510.27677
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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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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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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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Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification
2025-09-20 11:20:22Xulin Li, Yan Lu, Bin Liu, Jiaze Li, Qinhong Yang, Tao Gong, Qi Chu, Mang Ye, Nenghai YuAbstract
In real applications, person re-identification (ReID) is expected to retrieve the target person at any time, including both daytime and nighttime, ranging from short-term to long-term. However, existing ReID tasks and datasets can not meet this requirement, as they are constrained by available time and only provide training and evaluation for specific scenarios. Therefore, we investigate a new task called Anytime Person Re-identification (AT-ReID), which aims to achieve effective retrieval in multiple scenarios based on variations in time. To address the AT-ReID problem, we collect the first large-scale dataset, AT-USTC, which contains 403k images of individuals wearing multiple clothes captured by RGB and IR cameras. Our data collection spans 21 months, and 270 volunteers were photographed on average 29.1 times across different dates or scenes, 4-15 times more than current datasets, providing conditions for follow-up investigations in AT-ReID. Further, to tackle the new challenge of multi-scenario retrieval, we propose a unified model named Uni-AT, which comprises a multi-scenario ReID (MS-ReID) framework for scenario-specific features learning, a Mixture-of-Attribute-Experts (MoAE) module to alleviate inter-scenario interference, and a Hierarchical Dynamic Weighting (HDW) strategy to ensure balanced training across all scenarios. Extensive experiments show that our model leads to satisfactory results and exhibits excellent generalization to all scenarios.
Abstract (translated)
在实际应用中，人员重识别（ReID）期望能够随时检索目标人物，包括白天和黑夜，并且可以涵盖短期到长期的时间跨度。然而，现有的人员重识别任务和数据集无法满足这一要求，因为它们受限于可用时间并仅针对特定场景提供训练和评估。因此，我们研究了一个新的任务，称为任何时间人员重识别（AT-ReID），目标是在基于时间变化的多场景中实现有效的检索。 为了解决AT-ReID问题，我们收集了首个大规模数据集AT-USTC，该数据集包含403,000张由RGB和IR相机拍摄的不同个体身着多种衣物的照片。我们的数据收集工作历时21个月，并且平均每个志愿者（共270人）在不同日期或场景中被拍摄了29.1次，这比目前的数据集多了4到15倍的拍摄次数，从而为后续关于AT-ReID的研究提供了条件。 此外，为了应对多场景检索带来的新挑战，我们提出了一种统一模型Uni-AT。该模型包含一个多场景重识别（MS-ReID）框架用于特定场景特征学习、一个属性专家混合体（MoAE）模块以减轻跨场景干扰以及一个分层动态加权（HDW）策略以确保所有场景的平衡训练。 广泛的实验表明，我们的模型表现出令人满意的结果，并且在所有场景中都展示了出色的泛化能力。
URL
https://arxiv.org/abs/2509.16635
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A review of Recent Techniques for Person Re-Identification
2025-09-19 13:47:57Andrea Asperti, Salvatore Fiorilla, Simone Nardi, Lorenzo OrsiniAbstract
Person re-identification (ReId), a crucial task in surveillance, involves matching individuals across different camera views. The advent of Deep Learning, especially supervised techniques like Convolutional Neural Networks and Attention Mechanisms, has significantly enhanced person Re-ID. However, the success of supervised approaches hinges on vast amounts of annotated data, posing scalability challenges in data labeling and computational costs. To address these limitations, recent research has shifted towards unsupervised person re-identification. Leveraging abundant unlabeled data, unsupervised methods aim to overcome the need for pairwise labelled data. Although traditionally trailing behind supervised approaches, unsupervised techniques have shown promising developments in recent years, signalling a narrowing performance gap. Motivated by this evolving landscape, our survey pursues two primary objectives. First, we review and categorize significant publications in supervised person re-identification, providing an in-depth overview of the current state-of-the-art and emphasizing little room for further improvement in this domain. Second, we explore the latest advancements in unsupervised person re-identification over the past three years, offering insights into emerging trends and shedding light on the potential convergence of performance between supervised and unsupervised paradigms. This dual-focus survey aims to contribute to the evolving narrative of person re-identification, capturing both the mature landscape of supervised techniques and the promising outcomes in the realm of unsupervised learning.
Abstract (translated)
人员再识别（ReId）是监控领域的一项关键任务，它涉及在不同摄像头视角下匹配个体。随着深度学习的兴起，特别是卷积神经网络和注意力机制等监督技术的应用，人员再识别的效果得到了显著提升。然而，这些方法的成功很大程度上依赖于大量的标注数据，这对数据标签的可扩展性和计算成本提出了挑战。为了克服这些限制，最近的研究转向了无监督人员再识别领域。通过利用丰富的未标记数据，无监督方法旨在减少对成对标注数据的需求。尽管传统上落后于监督方法，近年来无监督技术已显示出令人鼓舞的发展，表明其与监督方法在性能上的差距正在缩小。 鉴于这一不断变化的格局，我们的综述追求两个主要目标。首先，我们回顾并分类了监督人员再识别领域的重大出版物，提供了当前最新技术水平的深入概述，并强调在这个领域进一步改进的空间有限。其次，我们探索了过去三年无监督人员再识别领域的最新进展，提供新兴趋势的见解，并阐明监督和无监督范式之间性能趋同的可能性。 这项双重焦点的综述旨在为不断发展的人员再识别叙事做出贡献，捕捉到监督技术成熟格局的同时，也关注无监督学习领域中充满希望的结果。
URL
https://arxiv.org/abs/2509.22690
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Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification
2025-09-15 05:10:43Haonan Shi, Yubin Wang, De Cheng, Lingfeng He, Nannan Wang, Xinbo GaoAbstract
Unsupervised visible-infrared person re-identification (USVI-ReID) aims to learn modality-invariant image features from unlabeled cross-modal person datasets by reducing the modality gap while minimizing reliance on costly manual annotations. Existing methods typically address USVI-ReID using cluster-based contrastive learning, which represents a person by a single cluster center. However, they primarily focus on the commonality of images within each cluster while neglecting the finer-grained differences among them. To address the limitation, we propose a Hierarchical Identity Learning (HIL) framework. Since each cluster may contain several smaller sub-clusters that reflect fine-grained variations among images, we generate multiple memories for each existing coarse-grained cluster via a secondary clustering. Additionally, we propose Multi-Center Contrastive Learning (MCCL) to refine representations for enhancing intra-modal clustering and minimizing cross-modal discrepancies. To further improve cross-modal matching quality, we design a Bidirectional Reverse Selection Transmission (BRST) mechanism, which establishes reliable cross-modal correspondences by performing bidirectional matching of pseudo-labels. Extensive experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate that the proposed method outperforms existing approaches. The source code is available at: this https URL.
Abstract (translated)
无监督可见光-红外行人重识别（USVI-ReID）的目标是从未标注的跨模态行人数据集中学习模态不变性图像特征，同时减少模态差距并尽量减少对成本高昂的人工注释的依赖。现有方法通常通过基于聚类的对比学习来解决 USVI-ReID 问题，这种方法用单个聚类中心表示一个人。然而，它们主要关注每个聚类内图像之间的共性而忽略了这些图像之间细微的区别。为了解决这一局限性，我们提出了一种层次身份学习（HIL）框架。 由于每个大聚类可能包含反映图间细微差异的多个较小子聚类，我们通过二次聚类为每个现有粗粒度聚类生成多个记忆库。此外，我们提出了多中心对比学习（MCCL），以细化表示方法，增强模内聚类并最小化跨模式差距。为了进一步提高跨模态匹配质量，我们设计了一种双向逆向选择传输（BRST）机制，通过进行伪标签的双向匹配来建立可靠的跨模态对应关系。 在 SYSU-MM01 和 RegDB 数据集上进行的大量实验表明，所提出的方法优于现有方法。源代码可在以下网址获取：此 https URL。
URL
https://arxiv.org/abs/2509.11587
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Contextualized Multimodal Lifelong Person Re-Identification in Hybrid Clothing States
2025-09-14 12:46:39Robert Long, Rongxin Jiang, Mingrui YanAbstract
Person Re-Identification (ReID) has several challenges in real-world surveillance systems due to clothing changes (CCReID) and the need for maintaining continual learning (LReID). Previous existing methods either develop models specifically for one application, which is mostly a same-cloth (SC) setting or treat CCReID as its own separate sub-problem. In this work, we will introduce the LReID-Hybrid task with the goal of developing a model to achieve both SC and CC while learning in a continual setting. Mismatched representations and forgetting from one task to the next are significant issues, we address this with CMLReID, a CLIP-based framework composed of two novel tasks: (1) Context-Aware Semantic Prompt (CASP) that generates adaptive prompts, and also incorporates context to align richly multi-grained visual cues with semantic text space; and (2) Adaptive Knowledge Fusion and Projection (AKFP) which produces robust SC/CC prototypes through the use of a dual-path learner that aligns features with our Clothing-State-Aware Projection Loss. Experiments performed on a wide range of datasets and illustrate that CMLReID outperforms all state-of-the-art methods with strong robustness and generalization despite clothing variations and a sophisticated process of sequential learning.
Abstract (translated)
人员再识别（Re-ID）在实际的监控系统中面临若干挑战，包括因穿着变化（CCReID）和持续学习的需求（LReID）。以往的方法要么开发专门针对单一应用场景的模型，这通常是基于相同衣物设置的情况；要么将穿着变化视为一个独立的问题来处理。在这项工作中，我们将介绍LReID-Hybrid任务，旨在开发一种能够在连续学习环境中同时适应相同衣物与不同衣物情景的模型。 在从一项任务过渡到另一项任务时出现表示不匹配和遗忘问题是很重要的挑战，我们通过CMLReID（基于CLIP框架的方法）来解决这一问题。该方法由两个新颖的任务组成：(1) 具有上下文感知语义提示（Context-Aware Semantic Prompt, CASP）的任务，它可以生成自适应的提示，并将上下文信息纳入其中以对齐多粒度视觉线索与语义文本空间；以及 (2) 通过使用双路径学习器并结合衣物状态感知投影损失来产生稳健相同/不同衣物原型的自适应知识融合和投影（Adaptive Knowledge Fusion and Projection, AKFP）任务。实验在广泛的数据集上进行，结果表明CMLReID方法不仅超越了所有现有的最先进的技术，在面对服装变化的情况下还能保持强大的鲁棒性和泛化能力，并且即使是在复杂的序列学习过程中也是如此。
URL
https://arxiv.org/abs/2509.11247
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A Re-ranking Method using K-nearest Weighted Fusion for Person Re-identification
2025-09-04 09:29:25Quang-Huy Che, Le-Chuong Nguyen, Gia-Nghia Tran, Dinh-Duy Phan, Vinh-Tiep NguyenAbstract
In person re-identification, re-ranking is a crucial step to enhance the overall accuracy by refining the initial ranking of retrieved results. Previous studies have mainly focused on features from single-view images, which can cause view bias and issues like pose variation, viewpoint changes, and occlusions. Using multi-view features to present a person can help reduce view bias. In this work, we present an efficient re-ranking method that generates multi-view features by aggregating neighbors' features using K-nearest Weighted Fusion (KWF) method. Specifically, we hypothesize that features extracted from re-identification models are highly similar when representing the same identity. Thus, we select K neighboring features in an unsupervised manner to generate multi-view features. Additionally, this study explores the weight selection strategies during feature aggregation, allowing us to identify an effective strategy. Our re-ranking approach does not require model fine-tuning or extra annotations, making it applicable to large-scale datasets. We evaluate our method on the person re-identification datasets Market1501, MSMT17, and Occluded-DukeMTMC. The results show that our method significantly improves Rank@1 and mAP when re-ranking the top M candidates from the initial ranking results. Specifically, compared to the initial results, our re-ranking method achieves improvements of 9.8%/22.0% in Rank@1 on the challenging datasets: MSMT17 and Occluded-DukeMTMC, respectively. Furthermore, our approach demonstrates substantial enhancements in computational efficiency compared to other re-ranking methods.
Abstract (translated)
在人员再识别中，重排名是一个关键步骤，通过优化检索结果的初始排序来提高整体准确性。以往的研究主要集中在单一视角图像特征上，这可能导致视角偏差以及姿态变化、视点改变和遮挡等问题。利用多视角特征表示一个人有助于减少视角偏差。在这项工作中，我们提出了一种高效的重排名方法，该方法通过使用K最近邻加权融合(KWF)法聚合邻居的特征来生成多视角特征。具体而言，我们假设从再识别模型中提取的同一种身份的特征高度相似。因此，我们在无监督的方式下选择K个相邻的特征以生成多视角特征。此外，这项研究还探讨了在特征聚合过程中权重的选择策略，使我们可以确定有效的策略。我们的重排名方法不需要对模型进行微调或添加额外注释，使其适用于大规模数据集。我们在Market1501、MSMT17和Occluded-DukeMTMC等人员再识别数据集中评估了该方法。结果表明，在初始排序的前M名候选人中重新排列时，我们的方法在Rank@1和mAP上显著提升。具体来说，与初始结果相比，我们提出的重排名方法分别在具有挑战性的MSMT17和Occluded-DukeMTMC数据集上实现了9.8%和22.0%的Rank@1改进。此外，我们的方法展示了比其他重排名方法更高的计算效率。
URL
https://arxiv.org/abs/2509.04050
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Background Matters Too: A Language-Enhanced Adversarial Framework for Person Re-Identification
2025-09-03 05:38:22Kaicong Huang, Talha Azfar, Jack M. Reilly, Thomas Guggisberg, Ruimin KeAbstract
Person re-identification faces two core challenges: precisely locating the foreground target while suppressing background noise and extracting fine-grained features from the target region. Numerous visual-only approaches address these issues by partitioning an image and applying attention modules, yet they rely on costly manual annotations and struggle with complex occlusions. Recent multimodal methods, motivated by CLIP, introduce semantic cues to guide visual understanding. However, they focus solely on foreground information, but overlook the potential value of background cues. Inspired by human perception, we argue that background semantics are as important as the foreground semantics in ReID, as humans tend to eliminate background distractions while focusing on target appearance. Therefore, this paper proposes an end-to-end framework that jointly models foreground and background information within a dual-branch cross-modal feature extraction pipeline. To help the network distinguish between the two domains, we propose an intra-semantic alignment and inter-semantic adversarial learning strategy. Specifically, we align visual and textual features that share the same semantics across domains, while simultaneously penalizing similarity between foreground and background features to enhance the network's discriminative power. This strategy drives the model to actively suppress noisy background regions and enhance attention toward identity-relevant foreground cues. Comprehensive experiments on two holistic and two occluded ReID benchmarks demonstrate the effectiveness and generality of the proposed method, with results that match or surpass those of current state-of-the-art approaches.
Abstract (translated)
人员再识别面临两大核心挑战：一是精确定位前景目标同时抑制背景噪声，二是从目标区域提取细微特征。众多仅依赖视觉的方法通过图像分割和应用注意力模块来解决这些问题，但它们依赖于昂贵的手动注释，并且在处理复杂遮挡时表现不佳。受CLIP方法启发的最近多模态方法引入了语义线索以指导视觉理解，然而这些方法仅仅关注前景信息，而忽略了背景线索的潜在价值。 受到人类感知机制的启发，我们认为，在再识别任务中，背景语义与前景语义同样重要。这是因为人在关注目标外观时往往会消除背景干扰。因此，本文提出了一种端到端框架，该框架在双分支跨模态特征提取管道内共同建模前景和背景信息。为了帮助网络区分这两个领域，我们提出了一种内部语义对齐和跨域语义对抗学习策略：具体而言，在不同领域的视觉与文本特征共享相同语义时进行对齐，同时惩罚前景与背景特征之间的相似度以增强网络的判别能力。这种策略促使模型积极抑制噪声背景区域并提高对身份相关的前景线索的关注。 在两个全景和两个遮挡再识别基准上的全面实验表明了所提出方法的有效性和通用性，并且其结果可匹敌或超越目前最先进的方法。
URL
https://arxiv.org/abs/2509.03032
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FedKLPR: Personalized Federated Learning for Person Re-Identification with Adaptive Pruning
2025-08-24 16:11:41Po-Hsien Yu, Yu-Syuan Tseng, Shao-Yi ChienAbstract
Person re-identification (Re-ID) is a fundamental task in intelligent surveillance and public safety. Federated learning (FL) offers a privacy-preserving solution by enabling collaborative model training without centralized data collection. However, applying FL to real-world re-ID systems faces two major challenges: statistical heterogeneity across clients due to non-IID data distributions, and substantial communication overhead caused by frequent transmission of large-scale models. To address these issues, we propose FedKLPR, a lightweight and communication-efficient federated learning framework for person re-identification. FedKLPR introduces four key components. First, the KL-Divergence Regularization Loss (KLL) constrains local models by minimizing the divergence from the global feature distribution, effectively mitigating the effects of statistical heterogeneity and improving convergence stability under non-IID conditions. Secondly, KL-Divergence-Prune Weighted Aggregation (KLPWA) integrates pruning ratio and distributional similarity into the aggregation process, thereby improving the robustness of the global model while significantly reducing communication overhead. Furthermore, sparse Activation Skipping (SAS) mitigates the dilution of critical parameters during the aggregation of pruned client models by excluding zero-valued weights from the update process. Finally, Cross-Round Recovery (CRR) introduces a dynamic pruning control mechanism that halts pruning when necessary, enabling deeper compression while maintaining model accuracy. Experimental results on eight benchmark datasets demonstrate that FedKLPR achieves significant communication reduction. Compared with the state-of-the-art, FedKLPR reduces 33\%-38\% communication cost on ResNet-50 and 20\%-40\% communication cost on ResNet-34, while maintaining model accuracy within 1\% degradation.
Abstract (translated)
人员重新识别（Re-ID）是智能监控和公共安全中的一个基本任务。联邦学习（FL）通过允许在不进行集中式数据收集的情况下协作模型训练，提供了一种保护隐私的解决方案。然而，在实际的Re-ID系统中应用FL面临着两个主要挑战：由于非独立同分布（non-IID）的数据分布导致客户端之间的统计异质性以及由大规模模型频繁传输引起的大量通信开销。 为了解决这些问题，我们提出了FedKLPR，这是一种轻量级且通信效率高的联邦学习框架，用于人员重新识别。FedKLPR引入了四个关键组件： 1. **KL散度正则化损失（KLL）**：通过最小化从全局特征分布的发散来约束本地模型，有效地减轻统计异质性的影响，并在非独立同分布条件下提高收敛稳定性。 2. **KL散度剪枝加权聚合（KLPWA）**：将剪枝比率和分布相似性集成到聚合过程中，从而提高了全局模型的鲁棒性，并显著减少了通信开销。 3. **稀疏激活跳过（SAS）**：通过在更新过程中排除零值权重，减轻了在合并经过修剪的客户端模型时关键参数被稀释的问题。 4. **跨轮次恢复（CRR）**：引入了一种动态剪枝控制机制，在必要时停止剪枝操作，使更深层次的压缩成为可能，同时保持模型准确性。 实验结果表明，在八个基准数据集上，FedKLPR实现了显著的通信量减少。与现有最佳方法相比，FedKLPR在ResNet-50上的通信成本降低了33%-38%，在ResNet-34上则减少了20%-40%的同时，保持了模型精度不超过1%的退化。
URL
https://arxiv.org/abs/2508.17431
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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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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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