We consider the generic problem of detecting low-level structures in images, which includes
segmenting the manipulated parts, identifying out-of-focus pixels, separating shadow …

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 presents a network to detect shadows by exploring and combining global context
in deep layers and local context in shallow layers of a deep convolutional neural network …

Shadow removal is an essential task for scene understanding. Many studies consider only
matching the image contents, which often causes two types of ghosts: color in-consistencies …

Shadow removal is a challenging task as it requires the detection/annotation of shadows as
well as semantic understanding of the scene. In this paper, we propose an automatic and …

In this paper we propose an attentive recurrent generative adversarial network (ARGAN) to
detect and remove shadows in an image. The generator consists of multiple progressive …

Shadow detection is a fundamental and challenging task, since it requires an understanding
of global image semantics and there are various backgrounds around shadows. This paper …

Shadow detection and shadow removal are fundamental and challenging tasks, requiring
an understanding of the global image semantics. This paper presents a novel deep neural …

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 …