UnShadowNet：光照评判器引导的对比学习阴影去除方法
UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal
AI总结：
针对成对阴影-无阴影训练数据难获取的问题，提出由光照评判器引导、基于对比学习的弱监督阴影去除框架UnShadowNet，可扩展至全监督设置，在多个公开数据集上性能超越现有最优方法。
摘要：
阴影是常见的自然现象，会严重阻碍计算机视觉感知系统在实际场景（如自动驾驶）中的性能。一种解决方案是在感知系统处理图像前消除图像中的阴影区域。然而，训练这类解决方案需要成对的对齐阴影图像与无阴影图像，这类数据难以获取。我们提出了一种采用对比学习训练的新型弱监督阴影去除框架UnShadowNet。该框架由三部分组成：负责去除提取出的阴影的DeShadower网络，在由光照评判器对抗训练的Illumination网络的引导下工作；以及用于进一步去除伪影的Refinement网络。我们表明，UnShadowNet可以轻松扩展到全监督设置，以利用可用的真实标注数据。在三个公开阴影数据集（ISTD、调整版ISTD、SRD）上，无论是弱监督还是全监督设置，UnShadowNet的性能均优于现有的最优方法。
英文摘要：
Shadows are frequently encountered natural phenomena that significantly hinder the performance of computer vision perception systems in practical settings, e.g., autonomous driving. A solution to this would be to eliminate shadow regions from the images before the processing of the perception system. Yet, training such a solution requires pairs of aligned shadowed and non-shadowed images which are difficult to obtain. We introduce a novel weakly supervised shadow removal framework UnShadowNet trained using contrastive learning. It is composed of a DeShadower network responsible for the removal of the extracted shadow under the guidance of an Illumination network which is trained adversarially by the illumination critic and a Refinement network to further remove artefacts. We show that UnShadowNet can be easily extended to a fully-supervised set-up to exploit the ground-truth when available. UnShadowNet outperforms existing state-of-the-art approaches on three publicly available shadow datasets (ISTD, adjusted ISTD, SRD) in both the weakly and fully supervised setups.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
