Person_Re-identification
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AttriBE: Quantifying Attribute Expressivity in Body Embeddings for Recognition and Identification
2026-04-29 21:41:35Basudha Pal, Siyuan Huang, Anirudh Nanduri, Zhaoyang Wang, Rama ChellappaAbstract
Person re-identification (ReID) systems that match individuals across images or video frames are essential in many real-world applications. However, existing methods are often influenced by attributes such as gender, pose, and body mass index (BMI), which vary in unconstrained settings and raise concerns related to fairness and generalization. To address this, we extend the notion of expressivity, defined as the mutual information between learned features and specific attributes, using a secondary neural network to quantify how strongly attributes are encoded. Applying this framework to three transformer-based ReID models on a large-scale visible-spectrum dataset, we find that BMI consistently shows the highest expressivity in deeper layers. Attributes in the final representation are ranked as BMI > Pitch > Gender > Yaw, and expressivity evolves across layers and training epochs, with pose peaking in intermediate layers and BMI strengthening with depth. We further extend the analysis to cross-spectral person identification across infrared modalities including short-wave, medium-wave, and long-wave infrared. In this setting, pitch becomes comparable to BMI and attribute trends increase monotonically across depth, suggesting increased reliance on structural cues when bridging modality gaps. Overall, the results show that transformer-based ReID embeddings encode a hierarchy of implicit attributes, with morphometric information persistently embedded and pose contributing more strongly under cross-spectral conditions.
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URL
https://arxiv.org/abs/2604.27218
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InterPartAbility: Text-Guided Part Matching for Interpretable Person Re-Identification
2026-04-29 19:18:36Shakeeb Murtaza, Aryan Shukla, Rajarshi Bhattacharya, Maguelonne Heritier, Eric GrangerAbstract
Text-to-image person re-identification (TI-ReID) relies on natural-language text description to retrieve top matching individuals from a large gallery of images. While recent large vision-language models (VLMs) achieve strong retrieval performance, their decisions remain largely uninterpretable. Existing interpretability approaches in TI-ReID rely solely on slot-attention to highlight attended regions, but fail to reliably bind visual regions to semantically meaningful concepts, limiting explanations to qualitative visualizations over a restricted vocabulary. This paper introduces InterPartAbility, an interpretable TI-ReID method that performs explicit part-wise matching and enables phrase-region grounding. A new open-vocabulary, lightweight supervision, patch-phrase interaction module (PPIM) is proposed to train a standard TI-ReID model with concept-level guidance. Concept-based part phrases provide evidence that encourages the model to attend to corresponding image regions. InterPartAbility further constrains CLIP ViT self-attention to produce spatially concentrated patch activations aligned with each part-level phrase, yielding grounded explanation maps. A quantitative interpretability protocol for TI-ReID is introduced by adapting perturbation-based evaluation metrics, including counterfactual region masking that measures retrieval degradation when top-ranked explanatory regions are removed. Empirical results\footnote{Our code is included in the supplementary materials and will be made public.} on challenging benchmarks like CUHK-PEDES and ICFG-PEDES show that InterPartAbility achieves state-of-the-art (SOTA) interpretability performance under these metrics, while sustaining competitive retrieval accuracy.
Abstract (translated)
URL
https://arxiv.org/abs/2604.27122
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CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID
2026-04-29 07:18:37Fengchun Zhang, Qiang Ma, Liuyu Xiang, Jinshan Lai, Tingxuan Huang, Jianwei HuAbstract
Federated domain generalization for person re-identification (FedDG-ReID) aims to collaboratively train a pedestrian retrieval model across multiple decentralized source domains such that it can generalize to unseen target environments without compromising raw data privacy. However, this task is significantly challenged by the inherent stylistic gaps across decentralized clients. Without global supervision, models easily succumb to shortcut learning where representations overfit to domain specific camera biases rather than universal identity features. We propose CO-EVO, a novel federated framework that resolves this semantic-style conflict through a co-evolutionary mechanism. On the semantic side, Camera-Invariant Semantic Anchoring (CSA) learns identity prompts with cross-camera consistency to establish purified and domain-agnostic anchors that filter out local imaging noise. On the visual side, Global Style Diversification (GSD), powered by a Global Camera-Style Bank (GCSB), synthesizes realistic perturbations to expand the visual boundaries of training data. The core of CO-EVO is its co-evolutionary loop where purified anchors act as gravitational centers to guide the image encoder toward robust anatomical attributes amidst diverse style variations. Extensive experiments demonstrate that CO-EVO achieves state-of-the-art (SOTA) performance, proving that the synergy between semantic purification and style expansion is essential for robust cross-domain generalization. Our code is available at: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2604.26363
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From Global to Local: Rethinking CLIP Feature Aggregation for Person Re-Identification
2026-04-24 03:37:21Aotian Zheng, Winston Sun, Bahaa Alattar, Vitaly Ablavsky, Jenq-Neng HwangAbstract
CLIP-based person re-identification (ReID) methods aggregate spatial features into a single global \texttt{[CLS]} token optimized for image-text alignment rather than spatial selectivity, making representations fragile under occlusion and cross-camera variation. We propose SAGA-ReID, which reconstructs identity representations by aligning intermediate patch tokens with anchor vectors parameterized in CLIP's text embedding space -- emphasizing spatially stable evidence while suppressing corrupted or absent regions, without requiring textual descriptions of individual images. Controlled experiments isolate the aggregation mechanism under two qualitatively distinct conditions -- synthetic masking, where identity signal is absent, and realistic human distractors, where an overlapping person introduces semantically confusing signal -- with SAGA's advantage over global pooling growing substantially as occlusion increases across both conditions. Benchmark evaluations confirm consistent gains over CLIP-ReID across standard and occluded settings, with the largest improvements where global pooling is most unreliable: up to +10.6 Rank-1 on occluded benchmarks. SAGA's aggregation outperforms dedicated sequential patch aggregation on a stronger backbone, confirming that structured reconstruction addresses a bottleneck that backbone quality and architectural complexity alone cannot resolve. Code available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2604.22190
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Temporal Prototyping and Hierarchical Alignment for Unsupervised Video-based Visible-Infrared Person Re-Identification
2026-04-23 06:26:07Zhiyong Li, Wei Jiang, Haojie Liu, Mingyu Wang, Wanchong Xu, Weijie MaoAbstract
Visible-infrared person re-identification (VI-ReID) enables cross-modality identity matching for all-day surveillance, yet existing methods predominantly focus on the image level or rely heavily on costly identity annotations. While video-based VI-ReID has recently emerged to exploit temporal dynamics for improved robustness, existing studies remain limited to supervised settings. Crucially, the unsupervised video VI-ReID problem, where models must learn from RGB and infrared tracklets without identity labels, remains largely unexplored despite its practical importance in real-world deployment. To bridge this gap, we propose HiTPro (Hierarchical Temporal Prototyping), a prototype-driven framework without explicit hard pseudo-label assignment for unsupervised video-based VI-ReID. HiTPro begins with an efficient Temporal-aware Feature Encoder that first extracts discriminative frame-level features and then aggregates them into a robust tracklet-level representation. Building upon these features, HiTPro first constructs reliable intra-camera prototypes via Intra-Camera Tracklet Prototyping by aggregating features from temporally partitioned sub-tracklets. Through Hierarchical Cross-Prototype Alignment, we perform a two-stage positive mining process: progressing from within-modality associations to cross-modality matching, enhanced by Dynamic Threshold Strategy and Soft Weight Assignment. Finally, {Hierarchical Contrastive Learning} progressively optimizes feature-prototype alignment across three levels: intra-camera discrimination, cross-camera same-modality consistency, and cross-modality invariance. Extensive experiments on HITSZ-VCM and BUPTCampus demonstrate that HiTPro achieves state-of-the-art performance under fully unsupervised settings, significantly outperforming adapted baselines and establishes a strong baseline for future research.
Abstract (translated)
URL
https://arxiv.org/abs/2604.21324
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Thinking Before Matching: A Reinforcement Reasoning Paradigm Towards General Person Re-Identification
2026-04-21 08:24:07Quan Zhang, Jingze Wu, Jialong Wang, Xiaohua Xie, Jianhuang Lai, Hongbo ChenAbstract
Learning identity-discriminative representations with multi-scene generality has become a critical objective in person re-identification (ReID). However, mainstream perception-driven paradigms tend to identify fitting from massive annotated data rather than identity-causal cues understanding, which presents a fragile representation against multiple disruptions. In this work, ReID-R is proposed as a novel reasoning-driven paradigm that achieves explicit identity understanding and reasoning by incorporating chain-of-thought into the ReID pipeline. Specifically, ReID-R consists of a two-stage contribution: (i) Discriminative reasoning warm-up, where a model is trained in a CoT label-free manner to acquire identity-aware feature understanding; and (ii) Efficient reinforcement learning, which proposes a non-trivial sampling to construct scene-generalizable data. On this basis, ReID-R leverages high-quality reward signals to guide the model toward focusing on ID-related cues, achieving accurate reasoning and correct responses. Extensive experiments on multiple ReID benchmarks demonstrate that ReID-R achieves competitive identity discrimination as superior methods using only 14.3K non-trivial data (20.9% of the existing data scale). Furthermore, benefit from inherent reasoning, ReID-R can provide high-quality interpretation for results.
Abstract (translated)
URL
https://arxiv.org/abs/2604.19218
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Causal Bootstrapped Alignment for Unsupervised Video-Based Visible-Infrared Person Re-Identification
2026-04-17 02:15:59Shuang Li, Jiaxu Leng, Changjiang Kuang, Mingpi Tan, Yu Yuan, Xinbo GaoAbstract
VVI-ReID is a critical technique for all-day surveillance, where temporal information provides additional cues beyond static images. However, existing approaches rely heavily on fully supervised learning with expensive cross-modality annotations, limiting scalability. To address this issue, we investigate Unsupervised Learning for VVI-ReID (USL-VVI-ReID), which learns identity-discriminative representations directly from unlabeled video tracklets. Directly extending image-based USL-VI-ReID methods to this setting with generic pretrained encoders leads to suboptimal performance. Such encoders suffer from weak identity discrimination and strong modality bias, resulting in severe intra-modality identity confusion and pronounced clustering granularity imbalance between visible and infrared modalities. These issues jointly degrade pseudo-label reliability and hinder effective cross-modality alignment. To address these challenges, we propose a Causal Bootstrapped Alignment (CBA) framework that explicitly exploits inherent video priors. First, we introduce Causal Intervention Warm-up (CIW), which performs sequence-level causal interventions by leveraging temporal identity consistency and cross-modality identity consistency to suppress modality- and motion-induced spurious correlations while preserving identity-relevant semantics, yielding cleaner representations for unsupervised clustering. Second, we propose Prototype-Guided Uncertainty Refinement (PGUR), which employs a coarse-to-fine alignment strategy to resolve cross-modality granularity mismatch, reorganizing under-clustered infrared representations under the guidance of reliable visible prototypes with uncertainty-aware supervision. Extensive experiments on the HITSZ-VCM and BUPTCampus benchmarks demonstrate that CBA significantly outperforms existing USL-VI-ReID methods when extended to the USL-VVI-ReID setting.
Abstract (translated)
URL
https://arxiv.org/abs/2604.15631
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Beyond Visual Cues: Semantic-Driven Token Filtering and Expert Routing for Anytime Person ReID
2026-04-16 14:49:30Jiaxuan Li, Xin Wen, Zhihang LiAbstract
Any-Time Person Re-identification (AT-ReID) necessitates the robust retrieval of target individuals under arbitrary conditions, encompassing both modality shifts (daytime and nighttime) and extensive clothing-change scenarios, ranging from short-term to long-term intervals. However, existing methods are highly relying on pure visual features, which are prone to change due to environmental and time factors, resulting in significantly performance deterioration under scenarios involving illumination caused modality shifts or cloth-change. In this paper, we propose Semantic-driven Token Filtering and Expert Routing (STFER), a novel framework that leverages the ability of Large Vision-Language Models (LVLMs) to generate identity consistency text, which provides identity-discriminative features that are robust to both clothing variations and cross-modality shifts between RGB and IR. Specifically, we employ instructions to guide the LVLM in generating identity-intrinsic semantic text that captures biometric constants for the semantic model driven. The text token is further used for Semantic-driven Visual Token Filtering (SVTF), which enhances informative visual regions and suppresses redundant background noise. Meanwhile, the text token is also used for Semantic-driven Expert Routing (SER), which integrates the semantic text into expert routing, resulting in more robust multi-scenario gating. Extensive experiments on the Any-Time ReID dataset (AT-USTC) demonstrate that our model achieves state-of-the-art results. Moreover, the model trained on AT-USTC was evaluated across 5 widely-used ReID benchmarks demonstrating superior generalization capabilities with highly competitive results. Our code will be available soon.
Abstract (translated)
URL
https://arxiv.org/abs/2604.15090
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Beyond Pedestrians: Caption-Guided CLIP Framework for High-Difficulty Video-based Person Re-Identification
2026-04-09 02:55:51Shogo Hamano, Shunya Wakasugi, Tatsuhito Sato, Sayaka NakamuraAbstract
In recent years, video-based person Re-Identification (ReID) has gained attention for its ability to leverage spatiotemporal cues to match individuals across non-overlapping cameras. However, current methods struggle with high-difficulty scenarios, such as sports and dance performances, where multiple individuals wear similar clothing while performing dynamic movements. To overcome these challenges, we propose CG-CLIP, a novel caption-guided CLIP framework that leverages explicit textual descriptions and learnable tokens. Our method introduces two key components: Caption-guided Memory Refinement (CMR) and Token-based Feature Extraction (TFE). CMR utilizes captions generated by Multi-modal Large Language Models (MLLMs) to refine identity-specific features, capturing fine-grained details. TFE employs a cross-attention mechanism with fixed-length learnable tokens to efficiently aggregate spatiotemporal features, reducing computational overhead. We evaluate our approach on two standard datasets (MARS and iLIDS-VID) and two newly constructed high-difficulty datasets (SportsVReID and DanceVReID). Experimental results demonstrate that our method outperforms current state-of-the-art approaches, achieving significant improvements across all benchmarks.
Abstract (translated)
URL
https://arxiv.org/abs/2604.07740
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Scale-Aware Vision-Language Adaptation for Extreme Far-Distance Video Person Re-identification
2026-04-05 16:53:24Ashwat Rajbhandari, Bharatesh ChakravarthiAbstract
Extreme far-distance video person re-identification (ReID) is particularly challenging due to scale compression, resolution degradation, motion blur, and aerial-ground viewpoint mismatch. As camera altitude and subject distance increase, models trained on close-range imagery degrade significantly. In this work, we investigate how large-scale vision-language models can be adapted to operate reliably under these conditions. Starting from a CLIP-based baseline, we upgrade the visual backbone from ViT-B/16 to ViT-L/14 and introduce backbone-aware selective fine-tuning to stabilize adaptation of the larger transformer. To address noisy and low-resolution tracklets, we incorporate a lightweight temporal attention pooling mechanism that suppresses degraded frames and emphasizes informative observations. We retain adapter-based and prompt-conditioned cross-view learning to mitigate aerial-ground domain shifts, and further refine retrieval using improved optimization and k-reciprocal re-ranking. Experiments on the DetReIDX stress-test benchmark show that our approach achieves mAP scores of 46.69 (A2G), 41.23 (G2A), and 22.98 (A2A), corresponding to an overall mAP of 35.73. These results show that large-scale vision-language backbones, when combined with stability-focused adaptation, significantly enhance robustness in extreme far-distance video person ReID.
Abstract (translated)
极端远距离视频行人重识别（ReID）由于尺度压缩、分辨率退化、运动模糊以及空中-地面视角不匹配而极具挑战性。随着摄像机高度和主体距离的增加，在近距图像上训练的模型性能会显著下降。本研究探索了如何调整大规模视觉-语言模型以在此类条件下可靠运行。我们从基于CLIP的基线出发，将视觉主干网络从ViT-B/16升级至ViT-L/14，并引入主干网络感知的选择性微调以稳定大型Transformer的适应过程。为应对噪声和低分辨率的轨迹段，我们融入了轻量级时序注意力池化机制，以抑制退化帧并突显信息丰富的观测。我们保留了基于适配器和提示条件化的跨视角学习策略以缓解空中-地面域偏移，并进一步通过改进的优化与k-倒数重排序技术精炼检索结果。在DetReIDX压力测试基准上的实验表明，我们的方法在A2G、G2A和A2A场景下分别达到46.69、41.23和22.98的mAP分数，整体mAP为35.73。这些结果证明，结合稳定性导向的适配策略，大规模视觉-语言主干网络能显著提升极端远距离视频行人ReID的鲁棒性。
URL
https://arxiv.org/abs/2604.04183
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CMCC-ReID: Cross-Modality Clothing-Change Person Re-Identification
2026-04-03 07:19:30Haoxuan Xu, Hanzi Wang, Guanglin NiuAbstract
Person Re-Identification (ReID) faces severe challenges from modality discrepancy and clothing variation in long-term surveillance scenario. While existing studies have made significant progress in either Visible-Infrared ReID (VI-ReID) or Clothing-Change ReID (CC-ReID), real-world surveillance system often face both challenges simultaneously. To address this overlooked yet realistic problem, we define a new task, termed Cross-Modality Clothing-Change Re-Identification (CMCC-ReID), which targets pedestrian matching across variations in both modality and clothing. To advance research in this direction, we construct a new benchmark SYSU-CMCC, where each identity is captured in both visible and infrared domains with distinct outfits, reflecting the dual heterogeneity of long-term surveillance. To tackle CMCC-ReID, we propose a Progressive Identity Alignment Network (PIA) that progressively mitigates the issues of clothing variation and modality discrepancy. Specifically, a Dual-Branch Disentangling Learning (DBDL) module separates identity-related cues from clothing-related factors to achieve clothing-agnostic representation, and a Bi-Directional Prototype Learning (BPL) module performs intra-modality and inter-modality contrast in the embedding space to bridge the modality gap while further suppressing clothing interference. Extensive experiments on the SYSU-CMCC dataset demonstrate that PIA establishes a strong baseline for this new task and significantly outperforms existing methods.
Abstract (translated)
行人重识别（ReID）在长期监控场景中面临模态差异与衣着变化的双重严峻挑战。尽管现有研究在可见光-红外ReID（VI-ReID）或衣着变化ReID（CC-ReID）单一方面已取得显著进展，但实际监控系统往往需同时应对这两种问题。针对这一被忽视却极具现实意义的问题，我们定义了一项新任务——跨模态衣着变化重识别（CMCC-ReID），旨在实现跨模态与衣着变化双重变异下的行人匹配。为推进该方向研究，我们构建了新基准数据集SYSU-CMCC，其中每个身份均以不同衣着在可见光与红外双模态下采集，以反映长期监控中的双重异构性。为应对CMCC-ReID挑战，我们提出渐进式身份对齐网络（PIA），通过逐步缓解衣着变化与模态差异问题。具体而言，双分支解耦学习（DBDL）模块将身份相关特征与衣着相关因素分离，以获得与衣着无关的表征；双向原型学习（BPL）模块在嵌入空间中进行模态内与模态间对比，在弥合模态差距的同时进一步抑制衣着干扰。在SYSU-CMCC数据集上的大量实验表明，PIA为该新任务建立了强基线，并显著超越现有方法。
URL
https://arxiv.org/abs/2604.02808
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FedCVU: Federated Learning for Cross-View Video Understanding
2026-03-23 07:26:55Shenghan Zhang, Run Ling, Ke Cao, Ao Ma, Zhanjie ZhangAbstract
Federated learning (FL) has emerged as a promising paradigm for privacy-preserving multi-camera video understanding. However, applying FL to cross-view scenarios faces three major challenges: (i) heterogeneous viewpoints and backgrounds lead to highly non-IID client distributions and overfitting to view-specific patterns, (ii) local distribution biases cause misaligned representations that hinder consistent cross-view semantics, and (iii) large video architectures incur prohibitive communication overhead. To address these issues, we propose FedCVU, a federated framework with three components: VS-Norm, which preserves normalization parameters to handle view-specific statistics; CV-Align, a lightweight contrastive regularization module to improve cross-view representation alignment; and SLA, a selective layer aggregation strategy that reduces communication without sacrificing accuracy. Extensive experiments on action understanding and person re-identification tasks under a cross-view protocol demonstrate that FedCVU consistently boosts unseen-view accuracy while maintaining strong seen-view performance, outperforming state-of-the-art FL baselines and showing robustness to domain heterogeneity and communication constraints.
Abstract (translated)
联邦学习（FL）作为一种有前景的隐私保护多摄像头视频理解范式已受到广泛关注。然而，将FL应用于跨视角场景面临三大挑战：（i）异构视角与背景导致客户端数据分布高度非独立同分布，且模型易过拟合至视角特定模式；（ii）本地分布偏差导致表征错位，阻碍跨视角语义一致性；（iii）大型视频架构带来高昂的通信开销。针对上述问题，我们提出FedCVU联邦框架，包含三个核心组件：VS-Norm通过保留归一化参数处理视角特定统计信息；CV-Align作为轻量级对比正则化模块提升跨视角表征对齐；SLA采用选择性层聚合策略在保持精度的同时降低通信成本。在跨视角协议下的动作理解与行人重识别任务中，大量实验表明FedCVU在维持已见视角性能的同时持续提升未见视角准确率，其表现优于现有联邦学习基线方法，并对领域异构性与通信约束展现出强鲁棒性。
URL
https://arxiv.org/abs/2603.21647
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ALADIN:Attribute-Language Distillation Network for Person Re-Identification
2026-03-23 02:05:22Wang Zhou, Boran Duan, Haojun Ai, Ruiqi Lan, Ziyue ZhouAbstract
Recent vision-language models such as CLIP provide strong cross-modal alignment, but current CLIP-guided ReID pipelines rely on global features and fixed prompts. This limits their ability to capture fine-grained attribute cues and adapt to diverse appearances. We propose ALADIN, an attribute-language distillation network that distills knowledge from a frozen CLIP teacher to a lightweight ReID student. ALADIN introduces fine-grained attribute-local alignment to establish adaptive text-visual correspondence and robust representation learning. A Scene-Aware Prompt Generator produces image-specific soft prompts to facilitate adaptive alignment. Attribute-local distillation enforces consistency between textual attributes and local visual features, significantly enhancing robustness under occlusions. Furthermore, we employ cross-modal contrastive and relation distillation to preserve the inherent structural relationships among attributes. To provide precise supervision, we leverage Multimodal LLMs to generate structured attribute descriptions, which are then converted into localized attention maps via CLIP. At inference, only the student is used. Experiments on Market-1501, DukeMTMC-reID, and MSMT17 show improvements over CNN-, Transformer-, and CLIP-based methods, with better generalization and interpretability.
Abstract (translated)
近期视觉语言模型（如CLIP）展现出强大的跨模态对齐能力，但当前基于CLIP引导的行人重识别（ReID）流程仍依赖全局特征与固定提示词，这限制了其捕捉细粒度属性线索及适应多样外观的能力。我们提出ALADIN——一种属性-语言蒸馏网络，可将冻结的CLIP教师模型的知识迁移至轻量级ReID学生模型。ALADIN引入细粒度属性-局部对齐机制，以建立自适应文本-视觉对应关系并实现鲁棒的表征学习。其场景感知提示生成器可产生图像相关的软提示词，促进自适应对齐；属性局部蒸馏则强制文本属性与局部视觉特征保持一致，显著提升遮挡场景下的鲁棒性。此外，我们采用跨模态对比蒸馏与关系蒸馏，以保留属性间的固有结构关系。为提供精确监督，我们利用多模态大语言模型生成结构化属性描述，再通过CLIP将其转换为局部化注意力图。推理阶段仅需学生网络。在Market-1501、DukeMTMC-reID与MSMT17数据集上的实验表明，该方法优于基于CNN、Transformer及CLIP的现有方案，具有更优泛化性与可解释性。
URL
https://arxiv.org/abs/2603.21482
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Vision-Language Attribute Disentanglement and Reinforcement for Lifelong Person Re-Identification
2026-03-20 06:23:05Kunlun Xu, Haotong Cheng, Jiangmeng Li, Xu Zou, Jiahuan ZhouAbstract
Lifelong person re-identification (LReID) aims to learn from varying domains to obtain a unified person retrieval model. Existing LReID approaches typically focus on learning from scratch or a visual classification-pretrained model, while the Vision-Language Model (VLM) has shown generalizable knowledge in a variety of tasks. Although existing methods can be directly adapted to the VLM, since they only consider global-aware learning, the fine-grained attribute knowledge is underleveraged, leading to limited acquisition and anti-forgetting capacity. To address this problem, we introduce a novel VLM-driven LReID approach named Vision-Language Attribute Disentanglement and Reinforcement (VLADR). Our key idea is to explicitly model the universally shared human attributes to improve inter-domain knowledge transfer, thereby effectively utilizing historical knowledge to reinforce new knowledge learning and alleviate forgetting. Specifically, VLADR includes a Multi-grain Text Attribute Disentanglement mechanism that mines the global and diverse local text attributes of an image. Then, an Inter-domain Cross-modal Attribute Reinforcement scheme is developed, which introduces cross-modal attribute alignment to guide visual attribute extraction and adopts inter-domain attribute alignment to achieve fine-grained knowledge transfer. Experimental results demonstrate that our VLADR outperforms the state-of-the-art methods by 1.9\%-2.2\% and 2.1\%-2.5\% on anti-forgetting and generalization capacity. Our source code is available at this https URL
Abstract (translated)
终身行人重识别（LReID）旨在从变化域中学习以获得统一的行人检索模型。现有LReID方法通常聚焦于从零开始或基于视觉分类预训练模型进行学习，而视觉语言模型（VLM）已在多种任务中展现出泛化知识。尽管现有方法可直接适配VLM，但由于它们仅考虑全局感知学习，细粒度属性知识未得到充分利用，导致知识获取与抗遗忘能力受限。针对此问题，我们提出一种新型VLM驱动的LReID方法，名为视觉语言属性解耦与增强（VLADR）。核心思想是显式建模人类通用共享属性以提升跨域知识迁移，从而有效利用历史知识强化新知识学习并缓解遗忘。具体而言，VLADR包含多粒度文本属性解耦机制，用于挖掘图像的全局与多样化局部文本属性。进而设计跨域跨模态属性增强方案，通过引入跨模态属性对齐引导视觉属性提取，并采用跨域属性对齐实现细粒度知识迁移。实验结果表明，我们的VLADR在抗遗忘与泛化能力上较最优方法提升1.9%-2.2%与2.1%-2.5%。源代码已公开于此https链接。
URL
https://arxiv.org/abs/2603.19678
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Homogeneous and Heterogeneous Consistency progressive Re-ranking for Visible-Infrared Person Re-identification
2026-03-17 06:32:53Yiming WangAbstract
Visible-infrared person re-identification faces greater challenges than traditional person re-identification due to the significant differences between modalities. In particular, the differences between these modalities make effective matching even more challenging, mainly because existing re-ranking algorithms cannot simultaneously address the intra-modal variations and inter-modal discrepancy in cross-modal person re-identification. To address this problem, we propose a novel Progressive Modal Relationship Re-ranking method consisting of two modules, called heterogeneous and homogeneous consistency re-ranking(HHCR). The first module, heterogeneous consistency re-ranking, explores the relationship between the query and the gallery modalities in the test set. The second module, homogeneous consistency reranking, investigates the intrinsic relationship within each modality between the query and the gallery in the test set. Based on this, we propose a baseline for cross-modal person re-identification, called a consistency re-ranking inference network (CRI). We conducted comprehensive experiments demonstrating that our proposed re-ranking method is generalized, and both the re-ranking and the baseline achieve state-of-the-art performance.
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URL
https://arxiv.org/abs/2603.16165
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BIT: Matching-based Bi-directional Interaction Transformation Network for Visible-Infrared Person Re-Identification
2026-03-15 06:33:00Haoxuan Xu, Guanglin NiuAbstract
Visible-Infrared Person Re-Identification (VI-ReID) is a challenging retrieval task due to the substantial modality gap between visible and infrared images. While existing methods attempt to bridge this gap by learning modality-invariant features within a shared embedding space, they often overlook the complex and implicit correlations between modalities. This limitation becomes more severe under distribution shifts, where infrared samples are often far fewer than visible ones. To address these challenges, we propose a novel network termed Bi-directional Interaction Transformation (BIT). Instead of relying on rigid feature alignment, BIT adopts a matching-based strategy that explicitly models the interaction between visible and infrared image pairs. Specifically, BIT employs an encoder-decoder architecture where the encoder extracts preliminary feature representations, and the decoder performs bi-directional feature integration and query aware scoring to enhance cross-modality correspondence. To our best knowledge, BIT is the first to introduce such pairwise matching-driven interaction in VI-ReID. Extensive experiments on several benchmarks demonstrate that our BIT achieves state-of-the-art performance, highlighting its effectiveness in the VI-ReID task.
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URL
https://arxiv.org/abs/2603.14243
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Multi-Grained Vision-Language Alignment for Domain Generalized Person Re-Identification
2026-03-14 16:33:12Jiachen Li, Xiaojin Gong, Dongping ZhangAbstract
Domain Generalized person Re-identification (DG Re-ID) is a challenging task, where models are trained on source domains but tested on unseen target domains. Although previous pure vision-based models have achieved significant progress, the performance remains further improved. Recently, Vision-Language Models (VLMs) present outstanding generalization capabilities in various visual applications. However, directly adapting a VLM to Re-ID shows limited generalization improvement. This is because the VLM only produces with global features that are insensitive to ID nuances. To tacle this problem, we propose a CLIP-based multi-grained vision-language alignment framework in this work. Specifically, several multi-grained prompts are introduced in language modality to describe different body parts and align with their counterparts in vision modality. To obtain fine-grained visual information, an adaptively masked multi-head self-attention module is employed to precisely extract specific part features. To train the proposed module, an MLLM-based visual grounding expert is employed to automatically generate pseudo labels of body parts for supervision. Extensive experiments conducted on both single- and multi-source generalization protocols demonstrate the superior performance of our approach. The implementation code will be released at this https URL.
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URL
https://arxiv.org/abs/2603.14012
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FedBPrompt: Federated Domain Generalization Person Re-Identification via Body Distribution Aware Visual Prompts
2026-03-13 11:29:36Xin Xu, Weilong Li, Wei Liu, Wenke Huang, Zhixi Yu, Bin Yang, Xiaoying Liao, Kui JiangAbstract
Federated Domain Generalization for Person Re-Identification (FedDG-ReID) learns domain-invariant representations from decentralized data. While Vision Transformer (ViT) is widely adopted, its global attention often fails to distinguish pedestrians from high similarity backgrounds or diverse viewpoints -- a challenge amplified by cross-client distribution shifts in FedDG-ReID. To address this, we propose Federated Body Distribution Aware Visual Prompt (FedBPrompt), introducing learnable visual prompts to guide Transformer attention toward pedestrian-centric regions. FedBPrompt employs a Body Distribution Aware Visual Prompts Mechanism (BAPM) comprising: Holistic Full Body Prompts to suppress cross-client background noise, and Body Part Alignment Prompts to capture fine-grained details robust to pose and viewpoint variations. To mitigate high communication costs, we design a Prompt-based Fine-Tuning Strategy (PFTS) that freezes the ViT backbone and updates only lightweight prompts, significantly reducing communication overhead while maintaining adaptability. Extensive experiments demonstrate that BAPM effectively enhances feature discrimination and cross-domain generalization, while PFTS achieves notable performance gains within only a few aggregation rounds. Moreover, both BAPM and PFTS can be easily integrated into existing ViT-based FedDG-ReID frameworks, making FedBPrompt a flexible and effective solution for federated person re-identification. The code is available at this https URL.
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URL
https://arxiv.org/abs/2603.12912
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FedARKS: Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration for Person Re-identification
2026-03-06 10:29:12Xin Xu, Binchang Ma, Zhixi Yu, Wei LiuAbstract
The application of federated domain generalization in person re-identification (FedDG-ReID) aims to enhance the model's generalization ability in unseen domains while protecting client data privacy. However, existing mainstream methods typically rely on global feature representations and simple averaging operations for model aggregation, leading to two limitations in domain generalization: (1) Using only global features makes it difficult to capture subtle, domain-invariant local details (such as accessories or textures); (2) Uniform parameter averaging treats all clients as equivalent, ignoring their differences in robust feature extraction capabilities, thereby diluting the contributions of high quality clients. To address these issues, we propose a novel federated learning framework, Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration (FedARKS), comprising two mechanisms: RK (Robust Knowledge) and KS (Knowledge Selection).
Abstract (translated)
在人员再识别（Person Re-Identification，ReID）中应用联合领域泛化（Federated Domain Generalization, FedDG-ReID），旨在增强模型在未见过领域的泛化能力的同时保护客户端数据隐私。然而，现有的主流方法通常依赖于全局特征表示和简单的平均操作来进行模型聚合，这导致了领域泛化中的两个限制：(1) 仅使用全局特征难以捕捉细微的、跨域不变的局部细节（如配饰或纹理）；(2) 统一参数平均将所有客户端视为等同，忽视它们在鲁棒特征提取能力上的差异，从而稀释了高质量客户端的贡献。为解决这些问题，我们提出了一种新颖的联邦学习框架——通过鲁棒和判别性知识选择与集成实现的联合聚合（Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration, FedARKS），该框架包含两个机制：RK（Robust Knowledge）和KS（Knowledge Selection）。
URL
https://arxiv.org/abs/2603.06122
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MSP-ReID: Hairstyle-Robust Cloth-Changing Person Re-Identification
2026-03-02 09:18:02Xiangyang He, Lin WanAbstract
Cloth-Changing Person Re-Identification (CC-ReID) aims to match the same individual across cameras under varying clothing conditions. Existing approaches often remove apparel and focus on the head region to reduce clothing bias. However, treating the head holistically without distinguishing between face and hair leads to over-reliance on volatile hairstyle cues, causing performance degradation under hairstyle changes. To address this issue, we propose the Mitigating Hairstyle Distraction and Structural Preservation (MSP) framework. Specifically, MSP introduces Hairstyle-Oriented Augmentation (HSOA), which generates intra-identity hairstyle diversity to reduce hairstyle dependence and enhance attention to stable facial and body cues. To prevent the loss of structural information, we design Cloth-Preserved Random Erasing (CPRE), which performs ratio-controlled erasing within clothing regions to suppress texture bias while retaining body shape and context. Furthermore, we employ Region-based Parsing Attention (RPA) to incorporate parsing-guided priors that highlight face and limb regions while suppressing hair features. Extensive experiments on multiple CC-ReID benchmarks demonstrate that MSP achieves state-of-the-art performance, providing a robust and practical solution for long-term person re-identification.
Abstract (translated)
服装变化人员再识别（CC-ReID）旨在跨不同摄像机条件下，在穿着条件不同的情况下匹配同一个人的身份。现有的方法通常会去除衣物，并专注于头部区域以减少着装偏差。然而，这种整体处理头部而不区分脸部和头发的方式导致过分依赖发型线索，从而在发型发生变化时性能下降。 为了解决这个问题，我们提出了减轻发型干扰并保持结构信息的框架（MSP）。具体来说，MSP引入了面向发型的数据增强技术（HSOA），该技术生成同一身份下的发型多样性以减少对发型的依赖，并且突出稳定的面部和身体线索。为了防止丢失结构信息，我们设计了一种保留衣物的随机擦除方法（CPRE），这种方法在服装区域进行比例控制的擦除操作，从而抑制纹理偏差同时保持体型和上下文信息。 此外，我们采用了基于区域解析注意的方法（RPA）来融入解析引导的先验知识，该方法突出面部和四肢区域，并抑制头发特征。在多个CC-ReID基准测试上的广泛实验表明，MSP达到了最先进的性能水平，为长期人员再识别提供了稳健且实用的解决方案。
URL
https://arxiv.org/abs/2603.01640
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