Understanding shadows from a single image consists of two types of task in previous
studies, containing shadow detection and shadow removal. In this paper, we present a multi …

This paper introduces training of shadow detectors under the large-scale dataset paradigm.
This was previously impossible due to the high cost of precise shadow annotation. Instead …

We introduce scGAN, a novel extension of conditional Generative Adversarial Networks
(GAN) tailored for the challenging problem of shadow detection in images. Previous …

The requirement for paired shadow and shadow-free images limits the size and diversity of
shadow removal datasets and hinders the possibility of training large-scale, robust shadow …

Conventional image outpainting methods usually treat unobserved areas as unknown and
extend the scene only in terms of semantic consistency thus overlooking the hidden …

In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in
computer vision and image understanding, with respect to the modeling, the inference and …

We propose a novel GAN-based framework for detecting shadows in images, in which a
shadow detection network (D-Net) is trained together with a shadow attenuation network (A …

In this paper, we present a novel shadow detection framework by investigating the mutual
complementary mechanisms contained in this specific task. Our method is based on a key …

Shadow removal is an essential task in computer vision and computer graphics. Recent
shadow removal approaches all train convolutional neural networks (CNN) on real paired …

We propose a novel deep learning method for shadow removal. Inspired by physical models
of shadow formation, we use a linear illumination transformation to model the shadow effects …