Robust Visible-Infrared Person Re-Identification Based on Polymorphic Mask and Wavelet Graph Convolutional Network

When deploying re-identification (ReID) models in the field of public safety, understanding the robustness of models to various types of corrupted images is crucial. Unfortunately, in the real world, images are always contaminated (e.g., noise, blur, and weather changes), which is ignored by existing visible-infrared person re-identification (VI-ReID) models. The performance of existing models tested in corrupted scenes is severely degraded. Therefore, learning corruption-invariant representations for corrupted images in VI-ReID is valuable and deserves further investigation. We design a polymorphic masked wavelet graph convolutional network for VI-ReID under corrupted scenes. Firstly, a cross-modality data augmentation algorithm is designed to construct a mixed image set that merges multi-modality attributes to improve robustness against interference. Secondly, a dual-branch network consisting of a global branch and a graph structure branch is designed. The global branch extracts overall information. While the graph structure branch is a wavelet-based graph convolutional module that utilizes the robustness of human structural information to corruptions and modalities, it can filter noise and extract discriminative features specifically targeted for cross-modality scenes. Finally, the global branch and the graph structure branch are integrated, and modality consistency loss is designed to match the branches with hetero-center triplet loss. Experiments show that our method can effectively alleviate degradation problems under corrupted environments such as noise, blur, digitization, and weather changes, and achieve state-of-the-art on corrupted datasets. Besides, it still maintains good performance on clean datasets, facilitating the reliable deployment of VI-ReID in real-world scenarios.