UCM-VeID V2: A Richer Dataset and A Pre-Training Method for UAV Cross-Modality Vehicle Re-Identification

Abstract

Cross-Modality Re-Identification (VT-ReID) aims to achieve around-the-clock target matching, benefiting from the strengths of both RGB and infrared (IR) modalities. However, the field is hindered by limited datasets, particularly for vehicle VT-ReID, and by challenges such as modality bias training (MBT), stemming from biased pre-training on ImageNet. To tackle the above issues, this paper introduces an dataset benchmark, named UCM-VeID V2, for vehicle VT-ReID, and proposes a new self-supervised pre-training method, Cross-Modality Patch-Mixed Self-Supervised Learning (PMSL). UCM-VeID V2 dataset features a significant increase in data volume, along with enhancements in multiple aspects. PMSL addresses MBT by learning modality-invariant features through Patch-Mixed Image Reconstruction (PMIR) and Modality Discrimination Adversarial Learning (MDAL), and enhances discriminability with Modality-Augmented Contrasting Cluster (MACC). Comprehensive experiments are carried out to validate the proposed method.