Estimating the risk level of adversarial examples is essential for safely deploying machine
learning models in the real world. One popular approach for physical-world attacks is to …

Recent deep learning methods have achieved promising results in image shadow removal.
However, most of the existing approaches focus on working locally within shadow and non …

Shadow removal, which aims to restore the background in the shadow regions, is
challenging due to the highly ill-posed nature. Most existing deep learning-based methods …

Shadow removal is still a challenging task due to its inherent background-dependent and
spatial-variant properties, leading to unknown and diverse shadow patterns. Even powerful …

In this paper, we aim to predict human eye fixation with view-free scenes based on an end-to-
end deep learning architecture. Although convolutional neural networks (CNNs) have made …

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 removal is a computer-vision task that aims to restore the image content in shadow
regions. While almost all recent shadow-removal methods require shadow-free images for …

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 …

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 …

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 …