Re-identification
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Lightweight Distillation of SAM 3 and DINOv3 for Edge-Deployable Individual-Level Livestock Monitoring and Longitudinal Visual Analytics
2026-04-29 19:25:22Haiyu Yang, Miel HostensAbstract
Foundation-model pipelines for individual-level livestock monitoring -- combining open-vocabulary detection, promptable video segmentation, and self-supervised visual embeddings -- have raised the accuracy ceiling of precision livestock farming (PLF), but their GPU memory budgets exceed the envelope of commodity edge accelerators. To close this gap, the 446M-parameter Perception Encoder (PE-ViT-L+) backbone of SAM 3 is distilled into a 40.66M-parameter multi-scale student through three mechanisms: a Feature Pyramid Network student encoder built on TinyViT-21M-512, a four-term direction-then-scale distillation loss, and backbone-substitution inference with sliding-window session pruning that bounds streaming GPU memory growth. The DINOv3 family includes a pre-distilled ViT-S/16 variant (21.6M parameters) released alongside a 6716M-parameter ViT-7B teacher; the ViT-S (21M) variant is adopted as the per-individual embedder. On the Edinburgh Pig dataset, the compressed pipeline reaches 92.29% MOTA and 96.15% IDF1 against the SAM 3 teacher (1.68- and 0.84-percentage-point losses), achieves a 7.77-fold reduction in system-level parameters and a 3.01-fold reduction in peak VRAM (19.52GB -> 6.49GB), and reaches 97.34% top-1 accuracy with 91.67% macro-F1 on nine-class pig behaviour classification. The pipeline fits inside an NVIDIA Jetson Orin NX 16GB envelope with 4.9GB of headroom, supporting a proposed -- but not yet empirically validated -- on-device embedding-pool re-identification mechanism whose per-individual footprint of approximately 94MB per animal per year produces a longitudinal visual record amenable to retrospective association with disease, lameness, reproductive, and growth outcome labels.
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URL
https://arxiv.org/abs/2604.27128
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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.
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URL
https://arxiv.org/abs/2604.27122
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3D-LENS: A 3D Lifting-based Elevated Novel-view Synthesis method for Single-View Aerial-Ground Re-Identification
2026-04-29 10:38:48William Grolleau, Astrid Sabourin, Guillaume Lapouge, Catherine AchardAbstract
Aerial-Ground Re-Identification (AG-ReID) is constrained by the viewpoint-domain gap, as drastic viewpoint disparities occlude or distort discriminative features, making cross-viewpoint image retrieval challenging. While existing methods rely on paired cross-view annotations, real-world deployments, such as wilderness search-and-rescue (SAR), often lack target-domain data, requiring retrieval from ground-level references alone. To our knowledge, we are the first to address this challenge by formalizing the Single-View AG-ReID (SV AG-ReID) setting, where models trained on a single real viewpoint must generalize to an unseen viewpoint. We propose 3D Lifting-based Elevated Novel-view Synthesis (3D-LENS), a unified framework combining geometrically-consistent novel view synthesis that leverages large-scale 3D mesh reconstruction, with a robust representation learning scheme to mitigate synthetic-to-real bias. Unlike 2D generative baselines that suffer from geometric inconsistencies or prior 3D methods that are restricted to class-specific templates, our approach ensures view-consistent synthesis across diverse categories without predefined templates that fail to capture fine-grained details, such as carried objects. Extensive experiments demonstrate that our method achieves state-of-the-art performance on SV AG-ReID scenarios. Code and data will be released at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2604.26520
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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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GateMOT: Q-Gated Attention for Dense Object Tracking
2026-04-29 07:04:29Mingjin Lv, Zelin Liu, Feifei Shao, Yi-Ping Phoebe Chen, Junqing Yu, Wei Yang, Zikai SongAbstract
While large models demonstrate the strong representational power of vanilla attention, this core mechanism cannot be directly applied to Dense Object Tracking: its quadratic all-to-all interactions are computationally prohibitive for dense motion estimation on high-resolution features. This mismatch prevents Dense Object Tracking from fully leveraging attention-based modeling in crowded and occlusion-heavy scenes. To address this challenge, we introduce GateMOT, an online tracking framework centered on Q-Gated Attention (Q-Attention), an efficient and spatially aware attention variant. Our key idea is to repurpose the Query from a similarity-conditioning term into a learnable gating unit. This Gating-Query (Gating-Q) produces a probabilistic gate that modulates Key features in an element-wise manner, enabling explicit relevance selection instead of costly global aggregation. Built on this mechanism, parallel Q-Attention heads transform one shared feature map into task-specific yet consistent representations for detection, motion, and re-identification, yielding a tightly coupled multi-task decoder with linear-complexity gating operations. GateMOT achieves state-of-the-art HOTA of 48.4, MOTA of 67.8, and IDF1 of 64.5 on BEE24, and demonstrates strong performance on additional Dense Object Tracking benchmarks. These results show that Q-Attention is a simple, effective, and transferable building block for attention-based tracking in dense tracking scenarios.
Abstract (translated)
URL
https://arxiv.org/abs/2604.26353
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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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HaS: Accelerating RAG through Homology-Aware Speculative Retrieval
2026-04-22 11:15:54Peng Peng, Weiwei Lin, Wentai Wu, Xinyang Wang, Yongheng LiuAbstract
Retrieval-Augmented Generation (RAG) expands the knowledge boundary of large language models (LLMs) at inference by retrieving external documents as context. However, retrieval becomes increasingly time-consuming as the knowledge databases grow in size. Existing acceleration strategies either compromise accuracy through approximate retrieval, or achieve marginal gains by reusing results of strictly identical queries. We propose HaS, a homology-aware speculative retrieval framework that performs low-latency speculative retrieval over restricted scopes to obtain candidate documents, followed by validating whether they contain the required knowledge. The validation, grounded in the homology relation between queries, is formulated as a homologous query re-identification task: once a previously observed query is identified as a homologous re-encounter of the incoming query, the draft is deemed acceptable, allowing the system to bypass slow full-database retrieval. Benefiting from the prevalence of homologous queries under real-world popularity patterns, HaS achieves substantial efficiency gains. Extensive experiments demonstrate that HaS reduces retrieval latency by 23.74% and 36.99% across datasets with only a 1-2% marginal accuracy drop. As a plug-and-play solution, HaS also significantly accelerates complex multi-hop queries in modern agentic RAG pipelines. Source code is available at: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2604.20452
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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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From Redaction to Restoration: Deep Learning for Medical Image Anonymization and Reconstruction
2026-04-13 12:15:52Adrienne Kline, Abhijit Gaonkar, Daniel Pittman, Chris Kuehn, Nils ForkertAbstract
Removing patient-specific information from medical images is crucial to enable sharing and open science without compromising patient identities. However, many methods currently used for deidentification have negative effects on downstream image analysis tasks because of removal of relevant but non-identifiable information. This work presents an end-to-end deep learning framework for transforming raw clinical image volumes into de-identified, analysis-ready datasets without compromising downstream utility. The methodology developed and tested in this work first detects and redacts regions likely to contain protected health information (PHI), such as burned-in text and metadata, and then uses a generative deep learning model to inpaint the redacted areas with anatomically and imaging plausible content. The proposed pipeline leverages a lightweight hybrid architecture, combining CRNN-based redaction with a latent-diffusion inpainting restoration module (Stable Diffusion 2). We evaluate the approach using both privacy-oriented metrics, which quantify residual PHI and success of redaction, and image-quality and task-based metrics, which assess the fidelity of restored volumes for representative deep learning applications. Our results suggest that the proposed method yields de-identified medical images that are visually coherent, maintaining fidelity for downstream models, while substantially reducing the risk of patient re-identification. By automating anonymization and image reconstruction within a single workflow, and dissemination of large-scale medical imaging collections, thereby lowering a key barrier to data sharing and multi-institutional collaboration in medical imaging AI.
Abstract (translated)
URL
https://arxiv.org/abs/2604.11376
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Jailbreaking the Matrix: Nullspace Steering for Controlled Model Subversion
2026-04-11 19:19:05Vishal Pramanik, Maisha Maliha, Susmit Jha, Sumit Kumar JhaAbstract
Large language models remain vulnerable to jailbreak attacks -- inputs designed to bypass safety mechanisms and elicit harmful responses -- despite advances in alignment and instruction tuning. We propose Head-Masked Nullspace Steering (HMNS), a circuit-level intervention that (i) identifies attention heads most causally responsible for a model's default behavior, (ii) suppresses their write paths via targeted column masking, and (iii) injects a perturbation constrained to the orthogonal complement of the muted subspace. HMNS operates in a closed-loop detection-intervention cycle, re-identifying causal heads and reapplying interventions across multiple decoding attempts. Across multiple jailbreak benchmarks, strong safety defenses, and widely used language models, HMNS attains state-of-the-art attack success rates with fewer queries than prior methods. Ablations confirm that nullspace-constrained injection, residual norm scaling, and iterative re-identification are key to its effectiveness. To our knowledge, this is the first jailbreak method to leverage geometry-aware, interpretability-informed interventions, highlighting a new paradigm for controlled model steering and adversarial safety circumvention.
Abstract (translated)
URL
https://arxiv.org/abs/2604.10326
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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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Are We Recognizing the Jaguar or Its Background? A Diagnostic Framework for Jaguar Re-Identification
2026-04-06 13:17:14Antonio Rueda-Toicen, Abigail Allen Martin, Daniil Morozov, Matin Mahmood, Alexandra Schild, Shahabeddin Dayani, Davide Panza, Gerard de MeloAbstract
Jaguar re-identification (re-ID) from citizen-science imagery can look strong on standard retrieval metrics while still relying on the wrong evidence, such as background context or silhouette shape, instead of the coat pattern that defines identity. We introduce a diagnostic framework for wildlife re-ID with two axes: a leakage-controlled context ratio, background/foreground, computed from inpainted background-only versus foreground-only images, and a laterality diagnostic based on cross-flank retrieval and mirror self-similarity. To make these diagnostics measurable, we curate a Pantanal jaguar benchmark with per-pixel segmentation masks and an identity-balanced evaluation protocol. We then use representative mitigation families, ArcFace fine-tuning, anti-symmetry regularization, and Lorentz hyperbolic embeddings, as case studies under the same evaluation lens. The goal is not only to ask which model ranks best, but also what visual evidence it uses to do so.
Abstract (translated)
URL
https://arxiv.org/abs/2604.09690
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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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Automated Segmentation and Tracking of Group Housed Pigs Using Foundation Models
2026-04-03 19:50:08Ye Bi, Bimala Acharya, David Rosero, Juan SteibelAbstract
Foundation models (FM) are reshaping computer vision by reducing reliance on task-specific supervised learning and leveraging general visual representations learned at scale. In precision livestock farming, most pipelines remain dominated by supervised learning models that require extensive labeled data, repeated retraining, and farm-specific tuning. This study presents an FM-centered workflow for automated monitoring of group-housed nursery pigs, in which pretrained vision-language FM serve as general visual backbones and farm-specific adaptation is achieved through modular post-processing. Grounding-DINO was first applied to 1,418 annotated images to establish a baseline detection performance. While detection accuracy was high under daytime conditions, performance degraded under night-vision and heavy occlusion, motivating the integration of temporal tracking logic. Building on these detections, short-term video segmentation with Grounded-SAM2 was evaluated on 550 one-minute video clips; after post-processing, over 80% of 4,927 active tracks were fully correct, with most remaining errors arising from inaccurate masks or duplicated labels. To support identity consistency over an extended time, we further developed a long-term tracking pipeline integrating initialization, tracking, matching, mask refinement, re-identification, and post-hoc quality control. This system was evaluated on a continuous 132-minute video and maintained stable identities throughout. On 132 uniformly sampled ground-truth frames, the system achieved a mean region similarity (J) of 0.83, contour accuracy (F) of 0.92, J&F of 0.87, MOTA of 0.99, and MOTP of 90.7%, with no identity switches. Overall, this work demonstrates how FM prior knowledge can be combined with lightweight, task-specific logic to enable scalable, label-efficient, and long-duration monitoring in pig production.
Abstract (translated)
基础模型（FM）正通过减少对特定任务监督学习的依赖并利用大规模学习的通用视觉表征，重塑计算机视觉领域。在精准畜牧业中，大多数流程仍以需要大量标注数据、反复重新训练和农场特定调优的监督学习模型为主。本研究提出了一种以FM为核心的工作流程，用于群养保育猪的自动化监测，其中预训练的视觉-语言FM作为通用视觉主干网络，并通过模块化后处理实现农场特定适应。首先在1418张标注图像上应用Grounding-DINO建立基线检测性能。尽管白天条件下检测准确率较高，但在夜视和严重遮挡场景下性能下降，因此集成了时序追踪逻辑。基于这些检测结果，在550段一分钟视频片段上评估了Grounded-SAM2的短期视频分割效果；经过后处理，4927条活跃追踪轨迹中超过80%完全正确，剩余错误主要源于掩码不准确或标签重复。为实现长期身份一致性，进一步开发了集成初始化、追踪、匹配、掩码精炼、重识别和事后质量控制的长期追踪流程。该系统在连续132分钟视频上进行了评估，全程保持稳定的身份识别。在132个均匀采样的真实帧上，系统达到平均区域相似度（J）0.83、轮廓准确率（F）0.92、J&F值0.87、MOTA指标0.99、MOTP指标90.7%，且无身份切换。总体而言，本研究展示了如何将FM先验知识与轻量级任务特定逻辑相结合，实现猪生产中可扩展、标签高效和长时程监测。
URL
https://arxiv.org/abs/2604.03426
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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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Towards Context-Aware Image Anonymization with Multi-Agent Reasoning
2026-03-29 19:06:43Robert Aufschl\"ager, Jakob Folz, Gautam Savaliya, Manjitha D Vidanalage, Michael Heigl, Martin SchrammAbstract
Street-level imagery contains personally identifiable information (PII), some of which is context-dependent. Existing anonymization methods either over-process images or miss subtle identifiers, while API-based solutions compromise data sovereignty. We present an agentic framework CAIAMAR (\underline{C}ontext-\underline{A}ware \underline{I}mage \underline{A}nonymization with \underline{M}ulti-\underline{A}gent \underline{R}easoning) for context-aware PII segmentation with diffusion-based anonymization, combining pre-defined processing for high-confidence cases with multi-agent reasoning for indirect identifiers. Three specialized agents coordinate via round-robin speaker selection in a Plan-Do-Check-Act (PDCA) cycle, enabling large vision-language models to classify PII based on spatial context (private vs. public property) rather than rigid category rules. The agents implement spatially-filtered coarse-to-fine detection where a scout-and-zoom strategy identifies candidates, open-vocabulary segmentation processes localized crops, and $IoU$-based deduplication ($30\%$ threshold) prevents redundant processing. Modal-specific diffusion guidance with appearance decorrelation substantially reduces re-identification (Re-ID) risks. On CUHK03-NP, our method reduces person Re-ID risk by $73\%$ ($R1$: $16.9\%$ vs. $62.4\%$ baseline). For image quality preservation on CityScapes, we achieve KID: $0.001$, and FID: $9.1$, significantly outperforming existing anonymization. The agentic workflow detects non-direct PII instances across object categories, and downstream semantic segmentation is preserved. Operating entirely on-premise with open-source models, the framework generates human-interpretable audit trails supporting EU's GDPR transparency requirements while flagging failed cases for human review.
Abstract (translated)
街景图像包含个人身份信息（PII），其中部分信息的识别依赖于具体情境。现有匿名化方法要么对图像过度处理，要么遗漏细微标识，而基于API的方案则损害数据主权。本研究提出一种智能体框架CAIAMAR（上下文感知图像匿名化多智能体推理），通过基于扩散的匿名化实现上下文感知的PII分割，结合高置信度案例的预定义处理与针对间接标识的多智能体推理。三个专业化智能体通过PDCA循环中的轮询发言选择机制协同工作，使大视觉语言模型能够基于空间上下文（私有与公共财产）而非僵化的类别规则对PII进行分类。智能体实施空间过滤的粗到精检测策略：通过“侦察-放大”识别候选区域，开放词汇分割处理局部裁剪，并采用基于交并比（IoU）的去重（30%阈值）避免冗余处理。模态特定的扩散引导与外观去关联显著降低了重识别（Re-ID）风险。在CUHK03-NP数据集上，该方法将行人重识别风险降低73%（R1指标：16.9% vs. 基线62.4%）。在CityScapes数据集上，图像质量保持达到KID：0.001、FID：9.1，显著优于现有匿名化方法。该智能体工作流可跨对象类别检测非直接PII实例，且下游语义分割任务得以保留。框架完全基于本地部署并使用开源模型，生成人类可读的审计追踪以满足欧盟GDPR透明度要求，同时标记失败案例供人工复核。
URL
https://arxiv.org/abs/2603.27817
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CHIRP dataset: towards long-term, individual-level, behavioral monitoring of bird populations in the wild
2026-03-26 14:57:31Alex Hoi Hang Chan, Neha Singhal, Onur Kocahan, Andrea Meltzer, Saverio Lubrano, Miyako H. Warrington, Michel Griesser, Fumihiro Kano, Hemal NaikAbstract
Long-term behavioral monitoring of individual animals is crucial for studying behavioral changes that occur over different time scales, especially for conservation and evolutionary biology. Computer vision methods have proven to benefit biodiversity monitoring, but automated behavior monitoring in wild populations remains challenging. This stems from the lack of datasets that cover a range of computer vision tasks necessary to extract biologically meaningful measurements of individual animals. Here, we introduce such a dataset (CHIRP) with a new method (CORVID) for individual re-identification of wild birds. The CHIRP (Combining beHaviour, Individual Re-identification and Postures) dataset is curated from a long-term population of wild Siberian jays studied in Swedish Lapland, supporting re-identification (re-id), action recognition, 2D keypoint estimation, object detection, and instance segmentation. In addition to traditional task-specific benchmarking, we introduce application-specific benchmarking with biologically relevant metrics (feeding rates, co-occurrence rates) to evaluate the performance of models in real-world use cases. Finally, we present CORVID (COlouR-based Video re-ID), a novel pipeline for individual identification of birds based on the segmentation and classification of colored leg rings, a widespread approach for visual identification of individual birds. CORVID offers a probability-based id tracking method by matching the detected combination of color rings with a database. We use application-specific benchmarking to show that CORVID outperforms state-of-the-art re-id methods. We hope this work offers the community a blueprint for curating real-world datasets from ethically approved biological studies to bridge the gap between computer vision research and biological applications.
Abstract (translated)
对个体动物进行长期行为监测对于研究不同时间尺度上发生的行为变化至关重要，尤其在保护生物学和进化生物学领域。计算机视觉方法已被证明有益于生物多样性监测，但在野生种群中实现自动化行为监测仍具挑战性。这源于缺乏涵盖多种计算机视觉任务的数据集，而这些任务是提取个体动物具有生物学意义的测量指标所必需的。在此，我们推出这样一个数据集（CHIRP）以及一种针对野生鸟类个体重识别的新方法（CORVID）。CHIRP（结合行为、个体重识别与姿态）数据集源自瑞典拉普兰地区对野生西伯兰松鸦的长期种群研究，支持重识别（re-id）、动作识别、2D关键点估计、目标检测和实例分割任务。除传统的任务特定基准测试外，我们还引入了基于生物学相关指标（取食率、共存率）的应用特定基准测试，以评估模型在真实场景中的性能。最后，我们提出了CORVID（基于颜色的视频重识别），这是一种基于彩色腿环分割与分类的新型鸟类个体识别流程——彩色腿环是鸟类视觉个体识别的常用方法。CORVID通过将检测到的彩色腿环组合与数据库匹配，提供基于概率的个体追踪方法。我们利用应用特定基准测试表明，CORVID优于最先进的重识别方法。我们希望本研究能为学界提供一个从伦理审批的生物研究中策划真实世界数据集的蓝图，以弥合计算机视觉研究与生物学应用之间的差距。
URL
https://arxiv.org/abs/2603.25524
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