提出统一的数据增强方法，提升跨光谱重识别效果
RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identification
- 基于朗伯模型，将模态差异归因于材料表面的线性变换
- 设计RLE增强策略，包含中等与剧烈两类变换，提升跨光谱泛化能力
- 适用于跨光谱行人重识别任务，尤其在数据有限时表现突出
本文致力于建模跨光谱重识别任务中的模态差异。基于朗伯模型，我们观察到非线性模态差异主要源于不同材料表面所受的多样化线性变换。由此出发，我们将所有跨光谱重识别的数据增强策略统一为模拟此类局部线性变换，并将其分为中等变换与剧烈变换两类。基于此观察，我们提出随机线性增强（RLE）策略，包含中等随机线性增强（MRLE）与剧烈随机线性增强（RRLE），以拓展两类变换的边界。MRLE在约束条件下生成满足原始线性相关性的多样图像变换，而RRLE则直接生成局部线性变换，无需依赖外部信息。实验结果不仅证明了RLE的优越性与有效性，也证实其作为跨光谱重识别通用数据增强方法的巨大潜力。代码已公开于 https://github.com/stone96123/RLE。
原文摘要 · Abstract (English)
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{https://github.com/stone96123/RLE}}.
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