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

ObjectiveVisible and infrared images are two commonly used modalities in intelligent transportation scenarios and play a key role in vehicle re-identification. However, differences in imaging mechanisms and spectral responses lead to inconsistent visual characteristics between these modalities, which limits cross-modal vehicle re-identification. To address this problem, this paper proposes a Channel Relation Spectra Alignment (CaRS-Align) method that uses channel relation spectra, rather than channel-wise features, as the alignment target. This strategy reduces interference caused by imaging style differences at the relational-structure level. Within each modality, a channel relation spectrum is constructed to capture stable and semantically coordinated channel-to-channel relationships through correlation modeling. At the cross-modal level, the correlation between the corresponding channel relation spectra of the two modalities is maximized to achieve consistent alignment of relational structures. Experiments on the public MSVR310 and RGBN300 datasets show that CaRS-Align outperforms existing state-of-the-art methods. For example, on MSVR310, under infrared-to-visible retrieval, CaRS-Align achieves a Rank-1 accuracy of 64.35%, which is 2.58% higher than advanced existing methods.MethodsCaRS-Align adopts a hierarchical optimization paradigm: (1) for each modality, a channel-channel relation spectrum is constructed by mining inter-channel dependencies, yielding a semantically coordinated relation matrix that preserves the organizational structure of semantic cues; (2) cross-modal consistency is achieved by maximizing the correlation between the relation spectra of the two modalities, enabling progressive optimization from intra-modal construction to cross-modal alignment; and (3) relation spectrum alignment is integrated with standard classification and retrieval objectives commonly used in re-identification to supervise backbone training for the vehicle re-identification model.Results and DiscussionsCompared with several state-of-the-art cross-modal re-identification methods on the RGBN300 and MSVR310 datasets, CaRS-Align demonstrates strong performance and achieves best or second-best results across both retrieval modes. As shown in (Table 1), on RGBN300 it attains 75.09% Rank-1 accuracy and 55.45% mean Average Precision (mAP) in the infrared-to-visible mode, and 76.60% Rank-1 accuracy 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 accuracy and 41.25% mAP in the visible-to-infrared mode, and 64.35% Rank-1 accuracy and 40.99% mAP in the infrared-to-visible mode. (Fig. 4) presents Top-10 retrieval results, where CaRS-Align reduces identity mismatches in both directions (Fig. 5) illustrates feature distance distributions, showing substantial overlap between intra-class and inter-class distances without CaRS-Align (Fig. 5(a)), whereas clearer separation is observed with CaRS-Align (Fig. 5(b)), confirming improved feature discrimination. These results indicate that modeling channel-level relational structures improves both retrieval modes, increases adaptability to modality shifts, and effectively reduces mismatches caused by cross-modal differences.ConclusionsThis paper proposes a visible-infrared cross-modal vehicle re-identification method based on CaRS-Align. Within each modality, a channel relation spectrum is constructed to preserve semantic co-occurrence structures. A CaRS-Align function is then designed to maximize the correlation between modalities, thereby achieving consistent alignment and improving cross-modal performance. Experiments on the MSVR310 and RGBN300 datasets demonstrate that CaRS-Align outperforms existing state-of-the-art methods in key metrics, including Rank-1 accuracy and mAP.