Soft shadow is more challenging to detect than hard shadow due to its ambiguous boundary. Existing shadow detection methods pay more attention to hard shadow scene since collecting and annotating hard shadow images is more effortless. Motivated by that soft shadow has similar characteristics with hard shadow, and many traditional hard shadow datasets are publicly available, we propose a novel soft shadow detection method (namely Soft-DA) based on adversarial learning and domain adaptation scheme. Specifically, we create a limited soft shadow dataset, containing 1K soft shadow images with various scenes and shapes. Note that we just only need to annotate 0.4K shadow masks for semi-supervised learning. Besides, to tackle obvious domain discrepancy and potential intention difference between different datasets and similar tasks, we first align data distributions between domains by feature adversarial adaptation. And then, we introduce a novel detector separation strategy to tackle the intention difference issue. In this way, Soft-DA can effectively detect soft shadow with only a small number of soft shadow annotations. Extensive experiments demonstrate that our method can achieve superior performance to state of the arts.

由于边界模糊，软阴影比硬阴影更具挑战性。现有的阴影检测方法更加关注硬阴影场景，因为收集和注释硬阴影图像更加容易。鉴于软阴影具有与硬阴影相似的特性，并且许多传统的硬阴影数据集是公开可用的，我们提出了一种基于对抗学习和领域自适应方案的新型软阴影检测方法（即Soft-DA）。具体来说，我们创建一个有限的软阴影数据集，其中包含具有各种场景和形状的1K软阴影图像。请注意，对于半监督学习，我们只需要注释0.4K荫罩即可。此外，要解决不同数据集和类似任务之间明显的领域差异和潜在意图差异，我们首先通过特征对抗适配来对齐域之间的数据分布。然后，我们介绍了一种新颖的检测器分离策略，以解决意图差异问题。这样，Soft-DA可以仅使用少量的软阴影注释来有效地检测软阴影。大量的实验表明，我们的方法可以实现比现有技术更好的性能。