Shadow removal from a single image is generally still an open problem. Most existing
learning-based methods use supervised learning and require a large number of paired …

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 …

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 …

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 …

In this paper, we propose a novel two-stage context-aware network named CANet for
shadow removal, in which the contextual information from non-shadow regions is transferred …

Existing shadow detection methods suffer from an intrinsic limitation in relying on limited
labeled datasets, and they may produce poor results in some complicated situations. To …

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 …

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 …

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 …