Re-identification
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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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Face anonymization preserving facial expressions and photometric realism
2026-03-18 10:16:12Luigi Celona, Simone Bianco, Raimondo SchettiniAbstract
The widespread sharing of face images on social media platforms and in large-scale datasets raises pressing privacy concerns, as biometric identifiers can be exploited without consent. Face anonymization seeks to generate realistic facial images that irreversibly conceal the subject's identity while preserving their usefulness for downstream tasks. However, most existing generative approaches focus on identity removal and image realism, often neglecting facial expressions as well as photometric consistency -- specifically attributes such as illumination and skin tone -- that are critical for applications like relighting, color constancy, and medical or affective analysis. In this work, we propose a feature-preserving anonymization framework that extends DeepPrivacy by incorporating dense facial landmarks to better retain expressions, and by introducing lightweight post-processing modules that ensure consistency in lighting direction and skin color. We further establish evaluation metrics specifically designed to quantify expression fidelity, lighting consistency, and color preservation, complementing standard measures of image realism, pose accuracy, and re-identification resistance. Experiments on the CelebA-HQ dataset demonstrate that our method produces anonymized faces with improved realism and significantly higher fidelity in expression, illumination, and skin tone compared to state-of-the-art baselines. These results underscore the importance of feature-aware anonymization as a step toward more useful, fair, and trustworthy privacy-preserving facial data.
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URL
https://arxiv.org/abs/2603.17567
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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.
Abstract (translated)
URL
https://arxiv.org/abs/2603.16165
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AI Evasion and Impersonation Attacks on Facial Re-Identification with Activation Map Explanations
2026-03-16 15:12:41Noe Claudel, Weisi Guo, Yang XingAbstract
Facial identification systems are increasingly deployed in surveillance and yet their vulnerability to adversarial evasion and impersonation attacks pose a critical risk. This paper introduces a novel framework for generating adversarial patches capable of both evasion and impersonation attacks against deep re-identification models across non-overlapping cameras. Unlike prior approaches that require iterative patch optimisation for each target, our method employs a conditional encoder-decoder network to synthesize adversarial patches in a single forward pass, guided by multi-scale features from source and target images. The patches are optimised with a dual adversarial objective comprising of pull and push terms. To enhance imperceptibility and aid physical deployment, we further integrate naturalistic patch generation using pre-trained latent diffusion models. Experiments on standard pedestrian (Market-1501, DukeMTMCreID) and facial recognition benchmarks (CelebA-HQ, PubFig) datasets demonstrate the effectiveness of the proposed method. Our adversarial evasion attacks reduce mean Average Precision from 90% to 0.4% in white-box settings and from 72% to 0.4% in black-box settings, showing strong cross-model generalization. In targeted impersonation attacks, our framework achieves a success rate of 27% on CelebA-HQ, competing with other patch-based methods. We go further to use clustering of activation maps to interpret which features are most used by adversarial attacks and propose a pathway for future countermeasures. The results highlight the practicality of adversarial patch attacks on retrieval-based systems and underline the urgent need for robust defense strategies.
Abstract (translated)
URL
https://arxiv.org/abs/2603.15396
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MedPriv-Bench: Benchmarking the Privacy-Utility Trade-off of Large Language Models in Medical Open-End Question Answering
2026-03-15 07:47:35Shaowei Guan, Yu Zhai, Hin Chi Kwok, Jiawei Du, Xinyu Feng, Jing Li, Harry Qin, Vivian HuiAbstract
Recent advances in Retrieval-Augmented Generation (RAG) have enabled large language models (LLMs) to ground outputs in clinical evidence. However, connecting LLMs with external databases introduces the risk of contextual leakage: a subtle privacy threat where unique combinations of medical details enable patient re-identification even without explicit identifiers. Current benchmarks in healthcare heavily focus on accuracy, ignoring such privacy issues, despite strict regulations like Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR). To fill this gap, we present MedPriv-Bench, the first benchmark specifically designed to jointly evaluate privacy preservation and clinical utility in medical open-ended question answering. Our framework utilizes a multi-agent, human-in-the-loop pipeline to synthesize sensitive medical contexts and clinically relevant queries that create realistic privacy pressure. We establish a standardized evaluation protocol leveraging a pre-trained RoBERTa-Natural Language Inference (NLI) model as an automated judge to quantify data leakage, achieving an average of 85.9% alignment with human experts. Through an extensive evaluation of 9 representative LLMs, we demonstrate a pervasive privacy-utility trade-off. Our findings underscore the necessity of domain-specific benchmarks to validate the safety and efficacy of medical AI systems in privacy-sensitive environments.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14265
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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.
Abstract (translated)
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.
Abstract (translated)
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.
Abstract (translated)
URL
https://arxiv.org/abs/2603.12912
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FC-Track: Overlap-Aware Post-Association Correction for Online Multi-Object Tracking
2026-03-13 08:00:26Cheng Ju, Zejing Zhao, Akio NamikiAbstract
Reliable multi-object tracking (MOT) is essential for robotic systems operating in complex and dynamic environments. Despite recent advances in detection and association, online MOT methods remain vulnerable to identity switches caused by frequent occlusions and object overlap, where incorrect associations can propagate over time and degrade tracking reliability. We present a lightweight post-association correction framework (FC-Track) for online MOT that explicitly targets overlap-induced mismatches during inference. The proposed method suppresses unreliable appearance updates under high-overlap conditions using an Intersection over Area (IoA)-based filtering strategy, and locally corrects detection-to-tracklet mismatches through appearance similarity comparison within overlapped tracklet pairs. By preventing short-term mismatches from propagating, our framework effectively mitigates long-term identity switches without resorting to global optimization or re-identification. The framework operates online without global optimization or re-identification, making it suitable for real-time robotic applications. We achieve 81.73 MOTA, 82.81 IDF1, and 66.95 HOTA on the MOT17 test set with a running speed of 5.7 FPS, and 77.52 MOTA, 80.90 IDF1, and 65.67 HOTA on the MOT20 test set with a running speed of 0.6 FPS. Specifically, our framework FC-Track produces only 29.55% long-term identity switches, which is substantially lower than existing online trackers. Meanwhile, our framework maintains state-of-the-art performance on the MOT20 benchmark.
Abstract (translated)
URL
https://arxiv.org/abs/2603.12758
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SDF-Net: Structure-Aware Disentangled Feature Learning for Opticall-SAR Ship Re-identification
2026-03-13 02:41:43Furui Chen, Han Wang, Yuhan Sun, Jianing You, Yixuan Lv, Zhuang Zhou, Hong Tan, Shengyang LiAbstract
Cross-modal ship re-identification (ReID) between optical and synthetic aperture radar (SAR) imagery is fundamentally challenged by the severe radiometric discrepancy between passive optical imaging and coherent active radar sensing. While existing approaches primarily rely on statistical distribution alignment or semantic matching, they often overlook a critical physical prior: ships are rigid objects whose geometric structures remain stable across sensing modalities, whereas texture appearance is highly modality-dependent. In this work, we propose SDF-Net, a Structure-Aware Disentangled Feature Learning Network that systematically incorporates geometric consistency into optical--SAR ship ReID. Built upon a ViT backbone, SDF-Net introduces a structure consistency constraint that extracts scale-invariant gradient energy statistics from intermediate layers to robustly anchor representations against radiometric variations. At the terminal stage, SDF-Net disentangles the learned representations into modality-invariant identity features and modality-specific characteristics. These decoupled cues are then integrated through a parameter-free additive residual fusion, effectively enhancing discriminative power. Extensive experiments on the HOSS-ReID dataset demonstrate that SDF-Net consistently outperforms existing state-of-the-art methods. The code and trained models are publicly available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2603.12588
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AR2-4FV: Anchored Referring and Re-identification for Long-Term Grounding in Fixed-View Videos
2026-03-08 18:24:56Teng Yan, Yihan Liu, Jiongxu Chen, Teng Wang, Jiaqi Li, Bingzhuo ZhongAbstract
Long-term language-guided referring in fixed-view videos is challenging: the referent may be occluded or leave the scene for long intervals and later re-enter, while framewise referring pipelines drift as re-identification (ReID) becomes unreliable. AR2-4FV leverages background stability for long-term referring. An offline Anchor Bank is distilled from static background structures; at inference, the text query is aligned with this bank to produce an Anchor Map that serves as persistent semantic memory when the referent is absent. An anchor-based re-entry prior accelerates re-capture upon return, and a lightweight ReID-Gating mechanism maintains identity continuity using displacement cues in the anchor frame. The system predicts per-frame bounding boxes without assuming the target is visible in the first frame or explicitly modeling appearance variations. AR2-4FV achieves +10.3% Re-Capture Rate (RCR) improvement and -24.2% Re-Capture Latency (RCL) reduction over the best baseline, and ablation studies further confirm the benefits of the Anchor Map, re-entry prior, and ReID-Gating.
Abstract (translated)
长期的基于语言指导的对象在固定视角视频中的识别是一项挑战，因为目标对象可能会被遮挡或长时间离开场景并在之后重新进入。帧级参考流水线在这种情况下容易出现偏差，因为重识别（ReID）变得不可靠。AR2-4FV利用背景稳定性来进行长期引用。 该系统通过从静态背景结构中离线提取一个锚点库（Anchor Bank），在推理过程中，文本查询与这个银行对齐以生成持续的语义记忆——锚图（Anchor Map）。当目标对象不在场时，此锚图充当持久性的语义存储。基于锚点的再进入先验加速了返回后的重新捕获，并且轻量级的身份门控机制利用锚帧中的位移线索来保持身份连续性。该系统预测每一帧的目标边界框，而不假设目标在第一帧中可见或显式地建模外观变化。 AR2-4FV与最佳基线相比，在再捕捉率（RCR）上提高了10.3%，并且减少了24.2%的再捕捉延迟（RCL）。消融研究进一步确认了锚图、再进入先验和身份门控机制的好处。
URL
https://arxiv.org/abs/2603.07758
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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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EdgeDAM: Real-time Object Tracking for Mobile Devices
2026-03-05 18:35:25Syed Muhammad Raza, Syed Murtaza Hussain Abidi, Khawar Islam, Muhammad Ibrahim, Ajmal Saeed MianAbstract
Single-object tracking (SOT) on edge devices is a critical computer vision task, requiring accurate and continuous target localization across video frames under occlusion, distractor interference, and fast motion. However, recent state-of-the-art distractor-aware memory mechanisms are largely built on segmentation-based trackers and rely on mask prediction and attention-driven memory updates, which introduce substantial computational overhead and limit real-time deployment on resource-constrained hardware; meanwhile, lightweight trackers sustain high throughput but are prone to drift when visually similar distractors appear. To address these challenges, we propose EdgeDAM, a lightweight detection-guided tracking framework that reformulates distractor-aware memory for bounding-box tracking under strict edge constraints. EdgeDAM introduces two key strategies: (1) Dual-Buffer Distractor-Aware Memory (DAM), which integrates a Recent-Aware Memory to preserve temporally consistent target hypotheses and a Distractor-Resolving Memory to explicitly store hard negative candidates and penalize their re-selection during recovery; and (2) Confidence-Driven Switching with Held-Box Stabilization, where tracker reliability and temporal consistency criteria adaptively activate detection and memory-guided re-identification during occlusion, while a held-box mechanism temporarily freezes and expands the estimate to suppress distractor contamination. Extensive experiments on five benchmarks, including the distractor-focused DiDi dataset, demonstrate improved robustness under occlusion and fast motion while maintaining real-time performance on mobile devices, achieving 88.2% accuracy on DiDi and 25 FPS on an iPhone 15. Code will be released.
Abstract (translated)
边缘设备上的单目标跟踪（SOT）是一项关键的计算机视觉任务，要求在遮挡、干扰物和快速运动的情况下，在视频帧中进行准确且连续的目标定位。然而，最近最先进的干扰感知内存机制大多基于分割追踪器，并依赖于掩码预测和注意力驱动的记忆更新，这带来了大量的计算开销，限制了其在资源受限硬件上的实时部署；同时，轻量级的跟踪器虽然能够保持高吞吐量，但在视觉相似的干扰物出现时容易发生漂移。为了解决这些挑战，我们提出了EdgeDAM，这是一种轻量级的检测引导追踪框架，它将干扰感知内存重新构架以适应边界框追踪，并在严格的边缘约束条件下进行设计。EdgeDAM引入了两种关键策略：（1）双缓冲器干扰感知内存(Dual-Buffer Distractor-Aware Memory, DAM)，该策略集成了一个最近感知记忆模块来保留时间上一致的目标假设，并且使用一个干扰物解析内存模块明确存储难例负样本并在恢复期间惩罚其重新选择；（2）信心驱动切换和保框稳定化机制，这种机制根据追踪器可靠性和时间一致性标准自适应地激活检测和记忆引导的重识别，在遮挡时同时引入了一个保框机制暂时冻结并扩展估计以抑制干扰物污染。在包括专注于干扰物的DiDi数据集在内的五个基准测试上进行了广泛的实验，结果显示EdgeDAM在保持移动设备实时性能的同时，提高了遮挡和快速运动下的鲁棒性，并且在DiDi数据集中达到了88.2%的准确率，在iPhone 15上的帧率为25FPS。代码将在稍后发布。
URL
https://arxiv.org/abs/2603.05463
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MOO: A Multi-view Oriented Observations Dataset for Viewpoint Analysis in Cattle Re-Identification
2026-03-04 17:36:26William Grolleau, Achraf Chaouch, Astrid Sabourin, Guillaume Lapouge, Catherine AchardAbstract
Animal re-identification (ReID) faces critical challenges due to viewpoint variations, particularly in Aerial-Ground (AG-ReID) settings where models must match individuals across drastic elevation changes. However, existing datasets lack the precise angular annotations required to systematically analyze these geometric variations. To address this, we introduce the Multi-view Oriented Observation (MOO) dataset, a large-scale synthetic AG-ReID dataset of $1,000$ cattle individuals captured from $128$ uniformly sampled viewpoints ($128,000$ annotated images). Using this controlled dataset, we quantify the influence of elevation and identify a critical elevation threshold, above which models generalize significantly better to unseen views. Finally, we validate the transferability to real-world applications in both zero-shot and supervised settings, demonstrating performance gains across four real-world cattle datasets and confirming that synthetic geometric priors effectively bridge the domain gap. Collectively, this dataset and analysis lay the foundation for future model development in cross-view animal ReID. MOO is publicly available at this https URL.
Abstract (translated)
动物再识别（ReID）在不同视角变化下面临重大挑战，尤其是在空地结合（AG-ReID）场景中，模型必须匹配高度变化显著条件下的个体。然而，现有的数据集缺乏精确的角度标注，无法系统分析这些几何变化。为解决这一问题，我们推出了多视图定向观测（MOO）数据集，这是一个大规模的合成AG-ReID数据集，包含1,000头牛个体，从128个均匀采样的视角捕获，总计有128,000张标注图像。利用这个受控的数据集，我们量化了高度变化的影响，并确定了一个临界的高度阈值，在此之上模型能够更好地泛化到未见过的视图中。最后，我们在零样本和监督设置下验证了其在四个真实世界牛数据集中向实际应用迁移的能力，证明在所有这些数据集中性能均有提升，并确认合成几何先验有效地弥合了域间差距。总的来说，该数据集及其分析为未来跨视角动物ReID模型的发展奠定了基础。MOO数据集可在此网址公开获取：[https://此链接应替换为实际发布地址]。
URL
https://arxiv.org/abs/2603.04314
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Degradation-based augmented training for robust individual animal re-identification
2026-03-04 15:20:00Thanos Polychronou, Luk\'a\v{s} Adam, Viktor Penchev, Kostas PapafitsorosAbstract
Wildlife re-identification aims to recognise individual animals by matching query images to a database of previously identified individuals, based on their fine-scale unique morphological characteristics. Current state-of-the-art models for multispecies re- identification are based on deep metric learning representing individual identities by fea- ture vectors in an embedding space, the similarity of which forms the basis for a fast automated identity retrieval. Yet very often, the discriminative information of individual wild animals gets significantly reduced due to the presence of several degradation factors in images, leading to reduced retrieval performance and limiting the downstream eco- logical studies. Here, starting by showing that the extent of this performance reduction greatly varies depending on the animal species (18 wild animal datasets), we introduce an augmented training framework for deep feature extractors, where we apply artificial but diverse degradations in images in the training set. We show that applying this augmented training only to a subset of individuals, leads to an overall increased re-identification performance, under the same type of degradations, even for individuals not seen during training. The introduction of diverse degradations during training leads to a gain of up to 8.5% Rank-1 accuracy to a dataset of real-world degraded animal images, selected using human re-ID expert annotations provided here for the first time. Our work is the first to systematically study image degradation in wildlife re-identification, while introducing all the necessary benchmarks, publicly available code and data, enabling further research on this topic.
Abstract (translated)
野生动物重识别旨在通过匹配查询图像与先前已确定个体的数据库，根据其细微独特的形态特征来辨认单个动物。目前最先进的多物种重识别模型基于深度度量学习，在嵌入空间中用特征向量表示个别身份，这些向量之间的相似性构成了快速自动身份检索的基础。然而，由于图片中存在的多种退化因素（如图像模糊、光照变化等），个体野生动物的鉴别信息往往显著降低，导致检索性能下降，并限制了下游生态学研究。 本文首先展示了这种性能下降的程度在很大程度上取决于动物物种（基于18个野外动物数据集）。随后，我们引入了一个增强训练框架，用于深度特征提取器，在此框架中，我们在训练集中应用多样且人工制造的退化因素。结果显示，即使仅对部分个体进行增强训练，也能显著提升整体重识别性能，并且这种改善在遇到相同类型的降质图像时依然有效，即使是那些未参与训练的个体也不例外。 通过在训练过程中引入多样的退化情况，我们能够在一个实际世界中的动物图像数据集上（该数据集首次提供了基于人类专家重新识别注释筛选），获得高达8.5%的Rank-1准确率提升。我们的研究是首个系统地探讨野生动物重识别中图像降质问题的研究，并且引入了所有必要的基准测试、公开可用的代码和数据，以促进这一领域进一步的研究。
URL
https://arxiv.org/abs/2603.04163
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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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From Visual to Multimodal: Systematic Ablation of Encoders and Fusion Strategies in Animal Identification
2026-02-28 21:27:38Vasiliy Kudryavtsev, Kirill Borodin, German Berezin, Kirill Bubenchikov, Grach Mkrtchian, Alexander RyzhkovAbstract
Automated animal identification is a practical task for reuniting lost pets with their owners, yet current systems often struggle due to limited dataset scale and reliance on unimodal visual cues. This study introduces a multimodal verification framework that enhances visual features with semantic identity priors derived from synthetic textual descriptions. We constructed a massive training corpus of 1.9 million photographs covering 695,091~unique animals to support this investigation. Through systematic ablation studies, we identified SigLIP2-Giant and E5-Small-v2 as the optimal vision and text backbones. We further evaluated fusion strategies ranging from simple concatenation to adaptive gating to determine the best method for integrating these modalities. Our proposed approach utilizes a gated fusion mechanism and achieved a Top-1 accuracy of 84.28\% and an Equal Error Rate of 0.0422 on a comprehensive test protocol. These results represent an 11\% improvement over leading unimodal baselines and demonstrate that integrating synthesized semantic descriptions significantly refines decision boundaries in large-scale pet re-identification.
Abstract (translated)
自动化动物识别是一项实用的任务，有助于将走失的宠物归还给主人。然而，目前的系统往往因数据集规模有限和依赖单一视觉线索而难以实现这一目标。本研究引入了一种多模态验证框架，该框架通过合成文本描述中提取的语义身份先验来增强视觉特征。我们构建了一个包含190万张照片的数据集，覆盖了695,091只独特动物，以支持这项研究。 通过对多种实验进行系统性分析，我们确定SigLIP2-Giant和E5-Small-v2是最佳的视觉和文本骨干网络。此外，我们还评估了一系列融合策略，从简单的拼接到自适应门控机制，以确定将这些模式整合的最佳方法。我们的提议方法使用了门控融合机制，并在全面测试协议下实现了84.28%的Top-1准确率和0.0422的等错误率。 与领先的单模态基线相比，这一结果提高了11%，证明了将合成语义描述整合到大规模宠物再识别任务中可以显著优化决策边界。
URL
https://arxiv.org/abs/2603.02270
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STMI: Segmentation-Guided Token Modulation with Cross-Modal Hypergraph Interaction for Multi-Modal Object Re-Identification
2026-02-28 15:07:10Xingguo Xu, Zhanyu Liu, Weixiang Zhou, Yuansheng Gao, Junjie Cao, Yuhao Wang, Jixiang Luo, Dell ZhangAbstract
Multi-modal object Re-Identification (ReID) aims to exploit complementary information from different modalities to retrieve specific objects. However, existing methods often rely on hard token filtering or simple fusion strategies, which can lead to the loss of discriminative cues and increased background interference. To address these challenges, we propose STMI, a novel multi-modal learning framework consisting of three key components: (1) Segmentation-Guided Feature Modulation (SFM) module leverages SAM-generated masks to enhance foreground representations and suppress background noise through learnable attention modulation; (2) Semantic Token Reallocation (STR) module employs learnable query tokens and an adaptive reallocation mechanism to extract compact and informative representations without discarding any tokens; (3) Cross-Modal Hypergraph Interaction (CHI) module constructs a unified hypergraph across modalities to capture high-order semantic relationships. Extensive experiments on public benchmarks (i.e., RGBNT201, RGBNT100, and MSVR310) demonstrate the effectiveness and robustness of our proposed STMI framework in multi-modal ReID scenarios.
Abstract (translated)
多模态对象重识别（ReID）的目标是利用不同模态提供的互补信息来检索特定的对象。然而，现有的方法往往依赖于硬令牌过滤或简单的融合策略，这可能导致判别线索的丢失和背景干扰的增加。为了解决这些挑战，我们提出了STMI，这是一种新颖的多模态学习框架，包含三个关键组成部分： 1. **分割引导特征调制（SFM）模块**：该模块利用SAM生成的掩码来增强前景表示并抑制背景噪声，通过可学习的注意力调节实现。 2. **语义令牌重分配（STR）模块**：此模块采用可学习查询令牌和自适应重分配机制，在不丢弃任何令牌的情况下提取紧凑且具有信息量的表示形式。 3. **跨模态超图交互（CHI）模块**：该模块构建了一个跨模态统一超图，以捕捉高阶语义关系。 在公共基准测试（例如RGBNT201、RGBNT100和MSVR310）上的广泛实验表明，我们提出的STMI框架在多模态ReID场景中具有有效性和鲁棒性。
URL
https://arxiv.org/abs/2603.00695
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