一致性作为无监督阴影去除的正则化项
Consistency as Regularization for Unsupervised Shadow Removal
- La Rochelle University（拉罗谢尔大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出无监督阴影去除框架ShadowCLR，利用带阴影图像间的一致性作为正则化，在多个基准上取得优于现有无监督方法的性能，无需阴影掩码或无阴影参考图像。
AI中文摘要：
阴影去除是许多视觉任务的重要预处理步骤，但现有监督方法需要成对的带阴影图像和无阴影图像，而无监督方法通常仍依赖阴影掩码或无阴影参考图像。我们提出ShadowCLR，一种直接从带阴影图像中学习阴影去除的无监督框架。我们的关键观察是：不同观测中的阴影会变化，而底层场景内容基本保持一致。因此，我们将带阴影图像间的一致性作为正则化项，鼓励模型恢复与场景一致的外观，同时抑制仅与阴影相关的变化。全局和局部一致性还使我们能够探索视觉相关图像，从不完全对齐的观测中学习，并将表示聚焦于共享的场景信息。在多个基准上的实验表明，ShadowCLR的性能与最先进的无监督方法相当，且通常更优，证明一致性可在无需阴影掩码或无阴影图像的情况下为阴影去除提供正则化。
英文摘要：
Shadow removal is an important preprocessing step for many vision tasks, yet existing supervised methods require paired shadow and shadow-free images, while unsupervised approaches often still rely on shadow masks or shadow-free references. We propose ShadowCLR, an unsupervised framework that learns shadow removal directly from shadow images. Our key observation is that shadows vary across observations while the underlying scene content remains largely consistent. We therefore use consistency across shadow observations as regularization, encouraging the model to recover scene-consistent appearance while suppressing shadow-specific variations. Global and local consistency further enable us to explore visually related images, learn from imperfectly aligned observations, and focus the representation on shared scene information. Experiments on multiple benchmarks show that ShadowCLR achieves competitive and often superior performance over state-of-the-art unsupervised methods, demonstrating that consistency can provide regularization for shadow removal without shadow masks or shadow-free images.
