Multi-scale Decomposed Convolution Refinement Network for Visible-Infrared Person Re-Identification

• URL: http://arxiv.org/abs/2608.16015v1
• Date: Mon, 17 Aug 2026 02:17:02 GMT
• Title: Multi-scale Decomposed Convolution Refinement Network for Visible-Infrared Person Re-Identification
• Abstract summary: Visible-infrared person re-identification (VI-ReID) suffers from cross-modal discrepancies and limited discriminative capabilities.<n>We propose MDCRNet, a Multi-scale Decomposed Convolution Refinement Network that enhances cross-modal feature learning and discriminative metric learning.
• Score: 11.621464807854816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Dual-Space Modality Consistency Learning for Universal Cross-Modal Re-Identification [10.822746613983464]
  Cross-modal Re-Identification (ReID) aims to retrieve the same identity across heterogeneous imaging modalities.<n>We propose a Dual-Space Modality Consistency Learning framework for universal cross-modal ReID.
  arXiv  Detail & Related papers  (2026-08-07T08:20:36Z)
• MO R-CNN: Multispectral Oriented R-CNN for Object Detection in Remote Sensing Image [16.689111713505486]
  We propose a lightweight framework for multi-spectral oriented detection featuring heterogeneous feature extraction network (HFEN), single modality supervision (SMS), and condition-based multimodal label fusion (CMLF)<n>Experiments on the DroneVehicle, VEDAI and OGSOD datasets prove the superiority of our method.
  arXiv  Detail & Related papers  (2025-09-21T07:38:54Z)
• Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification [81.3063589622217]
  Unsupervised visible-infrared person re-identification (USVI-ReID) aims to learn modality-invariant image features from unlabeled cross-modal person datasets.
  arXiv  Detail & Related papers  (2025-09-15T05:10:43Z)
• Graph-Based Uncertainty Modeling and Multimodal Fusion for Salient Object Detection [12.743278093269325]
  We propose a dynamic uncertainty propagation and multimodal collaborative reasoning network (DUP-MCRNet)<n>DUGC is designed to propagate uncertainty between layers through a sparse graph constructed based on spatial semantic distance.<n>MCF uses learnable modality gating weights to weightedly fuse the attention maps of RGB, depth, and edge features.
  arXiv  Detail & Related papers  (2025-08-28T04:31:48Z)
• Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention [59.19580789952102]
  This paper proposes a novel semi-supervised Multi-Scale Uncertainty and Cross-Teacher-Student Attention (MUCA) model for RS image semantic segmentation tasks.<n>MUCA constrains the consistency among feature maps at different layers of the network by introducing a multi-scale uncertainty consistency regularization.<n>MUCA utilizes a Cross-Teacher-Student attention mechanism to guide the student network, guiding the student network to construct more discriminative feature representations.
  arXiv  Detail & Related papers  (2025-01-18T11:57:20Z)
• Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification [11.664820595258988]
  Primary challenges in visible-infrared person re-identification arise from the differences between visible (vis) and infrared (ir) images. Existing methods often rely on horizontal partitioning to align part-level features, which can introduce inaccuracies. We propose a novel Prototype-Driven Multi-feature generation framework (PDM) aimed at mitigating cross-modal discrepancies.
  arXiv  Detail & Related papers  (2024-09-09T14:12:23Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition [59.29808528182607]
  Cross-resolution face recognition (CRFR) is important in intelligent surveillance and biometric forensics. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space. In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR.
  arXiv  Detail & Related papers  (2021-03-25T14:03:42Z)
• Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification [35.55895776505113]
  Multi-Scale Part-Aware Cascading framework (MSPAC) is formulated by aggregating multi-scale fine-grained features from part to global. Cross-modality correlations can thus be efficiently explored on salient features for distinctive modality-invariant feature learning.
  arXiv  Detail & Related papers  (2020-12-12T15:39:11Z)
• Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification [208.1227090864602]
  Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. Existing VI-ReID methods tend to learn global representations, which have limited discriminability and weak robustness to noisy images. We propose a novel dynamic dual-attentive aggregation (DDAG) learning method by mining both intra-modality part-level and cross-modality graph-level contextual cues for VI-ReID.
  arXiv  Detail & Related papers  (2020-07-18T03:08:13Z)
• When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks [57.0502745301132]
  We propose Micro-Dense Nets, a novel architecture with global residual learning and local micro-dense aggregations. Our micro-dense block can be integrated with neural architecture search based models to boost their performance.
  arXiv  Detail & Related papers  (2020-04-19T08:34:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.