A disentanglement method based on Mutual Information Minimization is introduced to minimize statistical dependence between modality-shared and modality-specific features from a probability distribution perspective and a Local Discriminative Attention Module is designed to adaptively focus on highly informative body parts such as head-shoulder ratio and torso patterns.
Abstract
Visible-Infrared Person Re-Identification (VI-ReID) is essential for all-weather surveillance in smart city applications. Existing methods still face two critical bottlenecks. First, significant modal discrepancies between visible and infrared images lead to serious distribution shifts, causing identity-irrelevant modality information to interfere with identity semantics. Second, decoupled shared features often lack focus on discriminative local regions, which limits the model's ability to capture fine-grained details. To address these issues, we propose a novel framework termed Modality Disentanglement and Local Discriminative Enhancement (MDLDE). We first introduce a disentanglement method based on Mutual Information Minimization to minimize statistical dependence between modality-shared and modality-specific features from a probability distribution perspective. Subsequently, a Local Discriminative Attention Module is designed to adaptively focus on highly informative body parts such as head-shoulder ratio and torso patterns. By reinforcing these localized cues, the model achieves robust fine-grained representations against complex backgrounds. Extensive experiments on the SYSU-MM01 datasets demonstrate the superiority of our method. On the SYSU-MM01 All-Search mode, we achieve 73.9% Rank-1 and 69.7% mAP, outperforming current state-of-the-art approaches.
Visible-infrared person re-identification (VI-ReID) suffers from cross-modal discrepancies and limited discriminative capabilities, leading to suboptimal recognition performance. Current approaches exhibit limitations in semantic mining, cross-modal fusion and feature constraints. To tackle these challenges, we propose MDCRNet, a Multi-scale Decomposed Convolution Refinement Network that enhances cross-modal feature learning and discriminative metric learning. Specifically, we introduce a Hierarchical Learning Module (HLM) containing four Hierarchical Decomposed Convolution Attention (HDCA) modules, each equipped with lightweight channel attention and multi-scale spatial perception blocks to capture multi-scale spatial dependencies. Moreover, we develop a Joint Discriminative Metric Loss (JDML) incorporating a novel Granularity Discriminative Loss (GDL) that simultaneously optimizes intra-identity compactness and inter-identity separability across modalities. Extensive experiments on SYSU-MM01 and RegDB datasets demonstrate that MDCRNet achieves state-of-the-art performance on both benchmarks. Code is available at https://github.com/Kevin-zms/MDCRNet.
Mingsheng Zheng, Zirui Jiang, Bo Liu et al.· 0 citations
Visible-Infrared Person Re-Identification (VI-ReID) remains a challenging task due to the significant modality gap between visible and infrared images, which hinders accurate cross-modality identity matching. Existing methods often struggle to balance modality invariance and feature discriminability. Most methods employ ImageNet-pretrained backbones that are heavily biased toward RGB statistics, causing the extracted cross-modal features to over-emphasize visible-spectrum information and weakening the understanding of infrared cues. To address this issue, some works introduce third-modality generation or grayscale-based augmentation, but these strategies either increase training complexity or still leave a non-negligible discrepancy from real infrared data. We propose a Progressively Biased Split Vision Transformer (PBSVT), which combines a split ViT backbone with progressive bias training to gradually reduce RGB-dominant bias while preserving modality-shared structure. Extensive experiments on SYSU-MM01 and RegDB show that PBSVT achieves state-of-the-art or highly competitive performance while introducing no additional inference cost. PBSVT obtains the best results on 8 of the 12 reported indicators, including 77.90% Rank-1 and 97.98% Rank-10 on SYSU-MM01 all-search, 83.74% Rank-1 on SYSU-MM01 indoor-search, and 92.89% Rank-1, 98.77% Rank-10, and 92.18% mAP on RegDB Visible-to-Infrared. These results demonstrate the effectiveness of progressive modality transition for robust VI-ReID representation learning.
Mengru Jiao, Xinyue Xu, Junfeng Zhang· International journal of pat...· 0 citations
Visible-infrared person re-identification (VI-ReID) aims to retrieve pedestrian images of the same identity across visible and infrared modalities, but remains challenging due to the large modality gap and unstable local correspondence. Existing methods mainly rely on visual cues, which may be insufficient when infrared images lack color and fine-grained texture information. To address this issue, this paper proposes a CLIP-Guided Progressive Body-Part Semantic Alignment Network, termed PBSA-Net. The proposed method introduces CLIP-derived textual semantics as modality-agnostic guidance for both global representation learning and local body-part feature extraction. Specifically, a global semantic branch first learns identity-level textual anchors to regularize global visual features. Then, a body-part semantic branch exploits identity-aware body-part prompt learning, multi-level feature fusion, and text-guided cross-attention to guide fine-grained local representation learning. A progressive three-stage optimization strategy is further adopted to decouple global semantic learning, body-part semantic correspondence learning, and retrieval-oriented feature optimization. Experiments on SYSU-MM01, RegDB, and LLCM demonstrate the effectiveness of PBSA-Net. It achieves 76.5% Rank-1 and 74.2% mAP on SYSU-MM01, 82.5% Rank-1 and 76.0% mAP on RegDB, and 61.8% Rank-1 and 65.8% mAP on LLCM. Ablation studies further show that the proposed body-part semantic alignment and progressive optimization provide complementary improvements.
Person re-identification (ReID) serves as a critical component in intelligent surveillance systems, aiming to match identities across disjoint camera networks. While traditional methods primarily rely on single-modal RGB imagery, they are often constrained by environmental challenges such as low illumination and occlusion. To overcome these limitations, the field is rapidly evolving toward cross-modal and multi-modal paradigms. This survey presents a comprehensive overview of this transition, systematically reviewing key cross-modal tasks including visible-infrared (VI-ReID), text-image (TI-ReID), sketch-based (Sketch-ReID), and the emerging Non-Line-of-Sight (NLOS) ReID, which extends perception beyond direct visibility. Furthermore, we examine tri-spectral and multi-modal fusion ReID, discussing how complementary information from diverse sensors enhances robustness. Beyond summarizing datasets, challenges, and methodologies, we propose a Transformer-based baseline framework for visible-infrared ReID, designed to effectively capture modality-invariant features. Finally, based on the current landscape, we outline several promising directions for future research.
Xiao Wang, Bing Wang, Bin Yang et al.· 0 citations
Visible-infrared person re-identification remains a challenging task due to inherent modality discrepancies between RGB and infrared images. Existing methods often struggle to effectively capture both modality-specific and modality-invariant features simultaneously, limiting their cross-modal matching performance.
This paper presents DMFNet (Diverse Mid-feature Network), a novel deep learning architecture that effectively harnesses intermediate shared features to bridge this cross-modal gap. DMFNet integrates two key modules: a Multi-layer Feature Cascade Module (MFCM) that aggregates discriminative features across different network stages, and a Dual Feature Generation Module (DFGM) that produces diverse intermediate representations through Instance-Batch Normalization variants.
Extensive experiments on the SYSU-MM01 and RegDB datasets demonstrate that DMFNet achieves state-of-the-art performance, with significant improvements in Rank-1 accuracy (up to 8.2% on SYSU-MM01 and 6.5% on RegDB) and mean Average Precision (mAP) over existing methods.
Our approach not only enhances cross-modal matching capabilities but also provides interpretable feature visualizations, offering valuable insights into the network's decision-making process. These results pave the way for more robust person re-identification systems in real-world surveillance scenarios, particularly in low-light conditions where traditional visible-only systems often fail.
Unsupervised visible-infrared person re-identification (USVI-ReID) is challenging due to the large modality gap and the lack of cross-modal identity annotations. Progressive association paradigms have been proposed to gradually bridge the gap, but they suffer from two critical bottlenecks: reliance on ambiguous global representations and unchecked propagation of pseudo-label noise in an open-loop manner. To address these issues, we propose Structural-Semantic Reciprocal Learning (SSRL), a framework that transforms open-loop association into a self-correcting closed-loop system. Structurally, we introduce Fine-grained Structural Decoupling (FSD) to extract discriminative body-part primitives as reliable spatial anchors, complementing ambiguous holistic silhouettes with spatially consistent structural details. Semantically, we design a Closed-loop Semantic Calibration (CSC) mechanism that reconstructs shared semantic prototypes at each epoch and feeds them back into the training loop, effectively filtering pseudo-label noise before the next clustering cycle. Through the reciprocal interaction between structural and semantic learning, SSRL achieves robust cross-modal representation. Extensive experiments demonstrate the competitive performance of SSRL against state-of-the-art USVI-ReID methods on both SYSU-MM01 and RegDB, notably surpassing several supervised counterparts on RegDB.
Moyao Tian, Shijia Liu, Yan Yang et al.· 0 citations