Adaptive Illumination-Invariant Synergistic Feature Integration in a Stratified Granular Framework for Visible-Infrared Re-Identification

• URL: http://arxiv.org/abs/2502.21163v1
• Date: Fri, 28 Feb 2025 15:42:58 GMT
• Title: Adaptive Illumination-Invariant Synergistic Feature Integration in a Stratified Granular Framework for Visible-Infrared Re-Identification
• Authors: Yuheng Jia, Wesley Armour,
• Abstract summary: Visible-Infrared Person Re-Identification (VI-ReID) plays a crucial role in applications such as search and rescue, infrastructure protection, and nighttime surveillance.<n>We propose textbfAMINet, an Adaptive Modality Interaction Network.<n>AMINet employs multi-granularity feature extraction to capture comprehensive identity attributes from both full-body and upper-body images.
• Score: 18.221111822542024
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Visible-Infrared Person Re-Identification (VI-ReID) plays a crucial role in applications such as search and rescue, infrastructure protection, and nighttime surveillance. However, it faces significant challenges due to modality discrepancies, varying illumination, and frequent occlusions. To overcome these obstacles, we propose \textbf{AMINet}, an Adaptive Modality Interaction Network. AMINet employs multi-granularity feature extraction to capture comprehensive identity attributes from both full-body and upper-body images, improving robustness against occlusions and background clutter. The model integrates an interactive feature fusion strategy for deep intra-modal and cross-modal alignment, enhancing generalization and effectively bridging the RGB-IR modality gap. Furthermore, AMINet utilizes phase congruency for robust, illumination-invariant feature extraction and incorporates an adaptive multi-scale kernel MMD to align feature distributions across varying scales. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach, achieving a Rank-1 accuracy of $74.75\%$ on SYSU-MM01, surpassing the baseline by $7.93\%$ and outperforming the current state-of-the-art by $3.95\%$.

Related papers

• DCEvo: Discriminative Cross-Dimensional Evolutionary Learning for Infrared and Visible Image Fusion [58.36400052566673]
  Infrared and visible image fusion integrates information from distinct spectral bands to enhance image quality. Existing approaches treat image fusion and subsequent high-level tasks as separate processes. We propose a Discriminative Cross- Dimension Evolutionary Learning Framework, termed DCEvo, which simultaneously enhances visual quality and perception accuracy.
  arXiv  Detail & Related papers  (2025-03-22T07:01:58Z)
• Cross-Modality Perturbation Synergy Attack for Person Re-identification [66.48494594909123]
  The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. Existing attack methods have primarily focused on the characteristics of the visible image modality. This study proposes a universal perturbation attack specifically designed for cross-modality ReID.
  arXiv  Detail & Related papers  (2024-01-18T15:56:23Z)
• Frequency Domain Modality-invariant Feature Learning for Visible-infrared Person Re-Identification [79.9402521412239]
  We propose a novel Frequency Domain modality-invariant feature learning framework (FDMNet) to reduce modality discrepancy from the frequency domain perspective. Our framework introduces two novel modules, namely the Instance-Adaptive Amplitude Filter (IAF) and the Phrase-Preserving Normalization (PPNorm)
  arXiv  Detail & Related papers  (2024-01-03T17:11:27Z)
• Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification [43.05147831905626]
  We propose a novel Transferring Modality-Aware Pedestrian Attentive Learning (TMPA) model. TMPA focuses on the pedestrian regions to efficiently compensate for missing modality-specific features. experiments conducted on the benchmark SYSU-MM01 and RegDB datasets demonstrated the effectiveness of our proposed TMPA model.
  arXiv  Detail & Related papers  (2023-12-12T07:15:17Z)
• MRCN: A Novel Modality Restitution and Compensation Network for Visible-Infrared Person Re-identification [36.88929785476334]
  We propose a novel Modality Restitution and Compensation Network (MRCN) to narrow the gap between the two modalities. Our method achieves 95.1% in terms of Rank-1 and 89.2% in terms of mAP on the RegDB dataset.
  arXiv  Detail & Related papers  (2023-03-26T05:03:18Z)
• Exploring Invariant Representation for Visible-Infrared Person Re-Identification [77.06940947765406]
  Cross-spectral person re-identification, which aims to associate identities to pedestrians across different spectra, faces a main challenge of the modality discrepancy. In this paper, we address the problem from both image-level and feature-level in an end-to-end hybrid learning framework named robust feature mining network (RFM) Experiment results on two standard cross-spectral person re-identification datasets, RegDB and SYSU-MM01, have demonstrated state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-02-02T05:24:50Z)
• Towards Homogeneous Modality Learning and Multi-Granularity Information Exploration for Visible-Infrared Person Re-Identification [16.22986967958162]
  Visible-infrared person re-identification (VI-ReID) is a challenging and essential task, which aims to retrieve a set of person images over visible and infrared camera views. Previous methods attempt to apply generative adversarial network (GAN) to generate the modality-consisitent data. In this work, we address cross-modality matching problem with Aligned Grayscale Modality (AGM), an unified dark-line spectrum that reformulates visible-infrared dual-mode learning as a gray-gray single-mode learning problem.
  arXiv  Detail & Related papers  (2022-04-11T03:03:19Z)
• Modality-Adaptive Mixup and Invariant Decomposition for RGB-Infrared Person Re-Identification [84.32086702849338]
  We propose a novel modality-adaptive mixup and invariant decomposition (MID) approach for RGB-infrared person re-identification. MID designs a modality-adaptive mixup scheme to generate suitable mixed modality images between RGB and infrared images. Experiments on two challenging benchmarks demonstrate superior performance of MID over state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-03T14:26:49Z)
• On Exploring Pose Estimation as an Auxiliary Learning Task for Visible-Infrared Person Re-identification [66.58450185833479]
  In this paper, we exploit Pose Estimation as an auxiliary learning task to assist the VI-ReID task in an end-to-end framework. By jointly training these two tasks in a mutually beneficial manner, our model learns higher quality modality-shared and ID-related features. Experimental results on two benchmark VI-ReID datasets show that the proposed method consistently improves state-of-the-art methods by significant margins.
  arXiv  Detail & Related papers  (2022-01-11T09:44:00Z)
• MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification [35.97494894205023]
  RGB-infrared cross-modality person re-identification (ReID) task aims to recognize the images of the same identity between the visible modality and the infrared modality. Existing methods mainly use a two-stream architecture to eliminate the discrepancy between the two modalities in the final common feature space. We present a novel multi-feature space joint optimization (MSO) network, which can learn modality-sharable features in both the single-modality space and the common space.
  arXiv  Detail & Related papers  (2021-10-21T16:45:23Z)
• 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)

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.