Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

@article{Wang2017StackedCG,
  title={Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal},
  author={Jifeng Wang and Xiang Li and Le Hui and Jian Yang},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2017},
  pages={1788-1797}
}
  • Jifeng WangXiang Li Jian Yang
  • Published 7 December 2017
  • Computer Science
  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Understanding shadows from a single image consists of two types of task in previous studies, containing shadow detection and shadow removal. [] Key Method Our framework is based on a novel STacked Conditional Generative Adversarial Network (ST-CGAN), which is composed of two stacked CGANs, each with a generator and a discriminator. Specifically, a shadow image is fed into the first generator which produces a shadow detection mask.

ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal

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A simple yet efficient target-consistency generative adversarial network (TC-GAN) for the shadow removal task in the unsupervised manner that achieves comparable performance with supervised shadow removal methods.

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