PORSCHE: Progressive Optimization and Robust Spatial Convolution for Hybrid Enhancement in Visible-Infrared Vehicle Re-Identification

Visible-infrared vehicle re-identification has become crucial for stable 24-h surveillance of Visual Internet of Things (VIoT). It aims to leverage the shared information between different modalities to retrieve specific objects. Previous works primarily focus on projecting images from two modalities into a common embedding space to measure their similarity scores. However, the inherent distribution discrepancies between different modalities often lead models to rely on spurious features that are unrelated to vehicle identity, making effective feature fusion challenging. To address this unique problem, we propose the Progressive Optimization and Robust Spatial Convolution for Hybrid Enhancement (PORSCHE) model, which reduces the negative effects of spurious correlations and biases toward training pairs. Specifically, we introduce the Patch-wise Matching (PAM) module, which performs initial coarse-grained alignment between different modalities. Building upon this foundation, we develop the Point-wise Matching (POM) module to achieve fine-grained discriminative alignment through precise point-level feature matching, thereby enhancing identity-specific representation. To optimize these complementary PAM and POM components effectively, we implement a progressive training strategy that hierarchically refines feature representations from local patches to global structures, ensuring stable learning of modality-invariant characteristics. This coarse-to-fine architecture enables gradual fusion and alignment across modalities at both patch and point levels, effectively capturing the essential discriminative features required for robust cross-modality retrieval. Extensive experimental results on MSVR310, WMVeID863, and RGBN300 benchmarks demonstrate the effectiveness of our proposed method. The code will be available at https://github.com/HowardLiu28/PORSCHE.