CaRS-Align: Channel Relation Spectra Alignment for Cross-Modal Vehicle Re-identification

SA Baihui; ZHUANG Jingyi; ZHENG Jinjie; ZHU Jianqing

doi:10.11999/JEIT250917

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SA Baihui, ZHUANG Jingyi, ZHENG Jinjie, ZHU Jianqing. CaRS-Align: Channel Relation Spectra Alignment for Cross-Modal Vehicle Re-identification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250917

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SA Baihui, ZHUANG Jingyi, ZHENG Jinjie, ZHU Jianqing. CaRS-Align: Channel Relation Spectra Alignment for Cross-Modal Vehicle Re-identification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250917

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CaRS-Align: Channel Relation Spectra Alignment for Cross-Modal Vehicle Re-identification

doi: 10.11999/JEIT250917 cstr: 32379.14.JEIT250917

College of Engineering, Huaqiao University, Quanzhou 362021, China

Funds: Fujian Province Science and Technology Empowering Police Research Initiative (2024Y0064), High-level Talent Innovation and Entrepreneurship Project of Quanzhou City (2023C013R)

Accepted Date: 2026-01-04
Rev Recd Date: 2026-01-04

Available Online: 2026-01-15

Abstract

Abstract

Objective Visible and infrared images, as two commonly used modalities in intelligent transportation scenarios, play an important role in vehicle re-identification. However, due to differences in imaging mechanisms and spectral responses, the visual characteristics of these two modalities are inconsistent, which limits cross-modality vehicle re-identification. To address this issue, this paper proposes a Channel Relation Spectra Alignment (CaRS-Align) method, which uses channel relation spectra instead of channel-wise features as the alignment target, thereby mitigating the interference caused by imaging style differences at the relational structure level. Specifically, within each modality, the channel relation spectrum is constructed to capture stable, semantically collaborative channel-to-channel relationships through robust correlation modeling. Then, at the cross-modal level, the correlation between the corresponding channel relation spectra of the two modalities is maximized, achieving consistent alignment of channel relation structures. Experiments show that on the public MSVR310 and RGBN300 datasets, the proposed CaRS-Align method outperforms existing state-of-the-art methods. For example, on the MSVR310 dataset, under infrared-to-visible retrieval, CaRS-Align achieves a Rank-1 accuracy of 64.35%, which is 2.58% higher than existing advanced methods. Methods CaRS-Align follows a hierarchical optimization paradigm: (i) for each modality, it constructs a channel–channel relation spectrum by mining inter-channel dependencies, yielding a semantically coordinated relation matrix that preserves the organizational structure of semantic cues; (ii) it aligns cross-modal consistency by maximizing the correlation between the modalities’ relation spectra, enabling progressive optimization from intra-modal construction to cross-modal alignment; and (iii) it integrates relation-spectrum alignment with standard classification and retrieval objectives commonly used in re-identification to supervise backbone training for the vehicle re-identification model. Results and Discussions Compared with several state-of-the-art cross-modal re-identification methods on the public RGBN300 and MSVR310 datasets, CaRS-Align exhibits strong performance, achieving best- or second-best results in both retrieval modes. As shown in (Table 1), on RGBN300 it attains 75.09% Rank-1 and 55.45% mean Average Precision (mAP) in the infrared-to-visible mode, and 76.60% Rank-1 and 56.12% mAP in the visible-to-infrared mode. As shown in (Table 2), similar advantages are observed on MSVR310, with 64.54% Rank-1 and 41.25% mAP in the visible-to-infrared mode, and 64.35% Rank-1 and 40.99% mAP in the infrared-to-visible mode. (Fig. 4) presents Top-10 retrievals, where CaRS-Align notably reduces identity mismatches in both directions; and (Fig. 5) shows feature distance distributions, with substantial overlap between intra- and inter-class distances without CaRS-Align (Fig. 5(a)) versus clear separation with CaRS-Align (Fig. 5(b)), confirming more discriminative features. Collectively, these results demonstrate that modeling channel-level relational structure improves both cross-modal retrieval modes, enhances adaptability to modality shifts, and effectively mitigates mismatches arising from cross-modal differences. Conclusions This paper proposes a visible-infrared cross-modality vehicle re-identification method based on Channel Relation Spectra Alignment (CaRS-Align). Concretely, the channel relation spectrum is constructed within each modality to preserve the semantic co-occurrence structures in the data. Then, a channel relation spectra alignment function is designed to maximize the correlation between the spectra of different modalities, thereby achieving consistent alignment and enhancing cross-modality performance. Experiments conducted on two public datasets, MSVR310 and RGBN300, demonstrate that CaRS-Align outperforms existing state-of-the-art methods in key metrics such as Rank-1 accuracy and mAP.
- Intelligent transportation,
- Cross-modal vehicle re-identification,
- Channel relation spectrum alignment

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References(40)

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