Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data
arXiv:1903.10683 · doi:10.1109/ICCV.2019.00256
Abstract
This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples. However, directly employing adversarial learning and cycle-consistency constraints is insufficient to learn the underlying relationship between the shadow and shadow-free domains, since the mapping between shadow and shadow-free images is not simply one-to-one. To address the problem, we formulate Mask-ShadowGAN, a new deep framework that automatically learns to produce a shadow mask from the input shadow image and then takes the mask to guide the shadow generation via re-formulated cycle-consistency constraints. Particularly, the framework simultaneously learns to produce shadow masks and learns to remove shadows, to maximize the overall performance. Also, we prepared an unpaired dataset for shadow removal and demonstrated the effectiveness of Mask-ShadowGAN on various experiments, even it was trained on unpaired data.
arXiv commentAccepted to ICCV 2019
References in corpus (2)
Cited by in corpus (10)
- Shadow Removal by a Lightness-Guided Network with Training on Unpaired Data
- Revisiting Shadow Detection: A New Benchmark Dataset for Complex World
- Learning from Synthetic Shadows for Shadow Detection and Removal
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- Unsupervised Portrait Shadow Removal via Generative Priors
- Instance Shadow Detection with A Single-Stage Detector
- UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal
- Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era
- Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification
- Shadow Feature Refinement Network: Progressive Feature Refinement based on Knowledge Distillation for Effective Shadow Removal