Coarse-to-Fine Cross-Modality Generation for Enhancing Vehicle Re-Identification with High-Fidelity Synthetic Data

Abstract

Due to the critical issues of privacy and partial occlusion, license plate information is not always available in vehicle recognition systems. Consequently, researchers have increasingly turned towards vehicle re-identification (reID) techniques to bridge the gap between cross-view camera systems. Despite the growing interest, one major challenge persists: the scarcity of authentic, large-scale training datasets. To address this challenge, this paper introduces a coarse-to-fine generation pipeline designed to synthesize high-fidelity vehicle data, thereby facilitating subsequent vehicle representation learning. Specifically, the proposed approach consists of three stages: Prompt Processing, Diffusion Fine-tuning, and Semantic Filtering. First, we collect detailed prompts from vehicle websites and companies with fine-grained vehicle prototype attributes. Next, we leverage the prior knowledge of these automotive prototypes to fine-tune diffusion models. Finally, to ensure the quality of the synthesized data, we employ pretrained vision-language models to filter out substandard images. Building upon the high-quality data generated by this pipeline, we validate the effectiveness using vanilla models. Extensive experimental evaluations demonstrate that our approach achieves competitive accuracy on public benchmarks such as VeRi-776, VehicleID and CityFlowV2, and is compatible with various model architectures.