Abstract
This paper makes a step towards modeling the modality discrepancy in the cross-spectral re-identification task. Based on the Lambertain model, we observe that the non-linear modality discrepancy mainly comes from diverse linear transformations acting on the surface of different materials. From this view, we unify all data augmentation strategies for cross-spectral re-identification by mimicking such local linear transformations and categorizing them into moderate transformation and radical transformation. By extending the observation, we propose a Random Linear Enhancement (RLE) strategy which includes Moderate Random Linear Enhancement (MRLE) and Radical Random Linear Enhancement (RRLE) to push the boundaries of both types of transformation. Moderate Random Linear Enhancement is designed to provide diverse image transformations that satisfy the original linear correlations under constrained conditions, whereas Radical Random Linear Enhancement seeks to generate local linear transformations directly without relying on external information. The experimental results not only demonstrate the superiority and effectiveness of RLE but also confirm its great potential as a general-purpose data augmentation for cross-spectral re-identification. The code is available at \textcolor{magenta}{\url{this https URL}}.
Abstract (translated)
本文朝着建模跨光谱重识别任务中的模态差异迈出了一步。基于兰贝特定律模型，我们观察到非线性模态差异主要来自于作用于不同材料表面的多种线性变换。从这个角度来看，我们将所有用于跨光谱重识别的数据增强策略统一起来，通过模拟这样的局部线性变换，并将其分类为适度变换和激进变换。通过对这一观察结果进行扩展，我们提出了一种随机线性增强（RLE）策略，其中包括中度随机线性增强（MRLE）和激进随机线性增强（RRLE），以推动两种类型变换的界限。中度随机线性增强旨在提供多样化的图像变换，在受限条件下满足原始的线性相关性；而激进随机线性增强则试图直接生成局部线性变换，不依赖外部信息。实验结果不仅展示了RLE的优越性和有效性，还证实了它作为跨光谱重识别通用数据增强方法的巨大潜力。代码可在\textcolor{magenta}{\url{此 https URL}}获取。
URL
https://arxiv.org/abs/2411.01225
