Visible-infrared person re-identification (VIPR) has great potential for intelligent transportation systems for constructing smart cities, but it is challenging to utilize due to the huge modal discrepancy between visible and infrared images. Although visible and infrared data can appear to be two domains, VIPR is not identical to domain adaptation as it can massively eliminate modal discrepancies. Because VIPR has complete identity information on both visible and infrared modalities, once the domain adaption is overemphasized, the discriminative appearance information on the visible and infrared domains would drain. For that, we propose a novel margin-based modal adaptive learning (MMAL) method for VIPR in this paper. On each domain, we apply triplet and label smoothing cross-entropy functions to learn appearance-discriminative features. Between the two domains, we design a simple yet effective marginal maximum mean discrepancy (M${}^3$D) loss function to avoid an excessive suppression of modal discrepancies to protect the features’ discriminative ability on each domain. As a result, our MMAL method could learn modal-invariant yet appearance-discriminative features for improving VIPR. The experimental results show that our MMAL method acquires state-of-the-art VIPR performance, e.g., on the RegDB dataset in the visible-to-infrared retrieval mode, the rank-1 accuracy is 93.24% and the mean average precision is 83.77%.

We propose a new benchmark for vehicle re-identification in mixed visible and infrared domains. Unlike cross-modal vehicle re-identification, we focus on a more realistic scenario, namely, mixed-modal vehicle re-identification, in which both probe and gallery sets contain visible and infrared images. We provide auto-cropped visible and infrared images, simulating data from actual surveillance systems. We design a mixed triplet loss function for model training. We report both mixed-modal and cross-modal retrieval performance. Our mixed method performs well in cross-modal retrieval, e.g., the Rank-1 identification rate is 79.19% in the visible-to-infrared retrieval mode.

Visible-infrared vehicle re-identification (VIVR) seeks to match vehicle images of the same identity taken by cameras of different modalities. The noticeable disparity between visible and infrared modalities leads to attention deviations, causing deep models to incorrectly focus on different local regions of vehicles in visible and infrared images. We observed that the spatial distributions of distinguishing local regions, such as logos, front windows, and wheels, exhibit similarity in average images obtained from both visible and infrared images. Based on this, we propose a modality-consistent attention (MCA) approach for VIVR. Unlike image-level attention, our MCA is identity-level attention that holistically emphasizes the distinguishing regions of a vehicle identity across multiple images captured from various viewpoints. Furthermore, we constrain the differences between the identity-level spatial attention masks resulting from visible and infrared modalities. This approach helps deep networks focus consistently on learning the distinguishing local characteristics of vehicles across different modalities and viewpoints. Our experiments on RGBN300 and MSVR310 datasets demonstrate that our approach achieves state-of-the-art performance.

Omnidirectional image super-resolution (ODISR) is critical for VR/AR applications, as high-quality 360° visual content significantly enhances immersive experiences. However, existing ODISR methods suffer from limited receptive fields and high computational complexity, which restricts their ability to model long-range dependencies and extract global structural features. Consequently, these limitations hinder the effective reconstruction of high-frequency details. To address these issues, we propose a novel Mamba-based ODISR network, termed MambaOSR, which consists of three key modules working collaboratively for accurate reconstruction. Specifically, we first introduce a spatial-frequency visual state space model (SF-VSSM) to capture global contextual information via dual-domain representation learning, thereby enhancing the preservation of high-frequency details. Subsequently, we design a distortion-guided module (DGM) that leverages distortion map priors to adaptively model geometric distortions, effectively suppressing artifacts resulting from equirectangular projections. Finally, we develop a multi-scale feature fusion module (MFFM) that integrates complementary features across multiple scales, further improving reconstruction quality. Extensive experiments conducted on the SUN360 dataset demonstrate that our proposed MambaOSR achieves a 0.16 dB improvement in WS-PSNR and increases the mutual information by 1.99% compared with state-of-the-art methods, significantly enhancing both visual quality and the information richness of omnidirectional images.