Uniform Light Transformer for Person Re-identification under Complex Illumination

作者：Xiang Guo, Ruimin Hu, Dongliang Zhu, Mei Wang · 发表于：ACM Transactions on Multimedia Computing Communications and Applications · 年份：2025 · DOI：10.1145/3745786 · 研究领域：Video Surveillance and Tracking Methods、Image Enhancement Techniques、Infrared Target Detection Methodologies

The quality of pedestrian image retrieval is affected by the difference in illumination between images. Previous studies have used one-to-one lighting transformers to convert images taken under different lighting conditions into the target lighting. However, can we use a single lighting transformer to convert input images with various lighting conditions to the target lighting? This question motivated us to investigate the discrepancy between images generated by a Unified Lighting Transformer and the ground truth images across different illumination scales. We discovered that the modeling capability of the Unified Lighting Transformer for low-frequency information decreases gradually with an increase in the number of illuminant variations. Therefore, based on this insight, we proposed a Discriminative Feature Spectrum Consistency and Low-Frequency Information Constrained method. This method employs two constraints to enhance the Unified Lighting Transformer’s modeling capability for low-frequency information. The first mechanism enforces the constraint at the feature level by comparing the spectrum information between real and fake discriminative features. The second approach constrains the differences in pedestrian recognition features caused by the differences in low-frequency information between real and virtual images composed of low-frequency information from fake images and high-frequency information from authentic images. Our experiments show that our method outperform...