This work reviews the results of the NTIRE 2024 Challenge on Shadow Removal. Building
on the last year edition the current challenge was organized in two tracks with a track …

Recent deep learning methods have achieved promising results in image shadow removal.
However, their restored images still suffer from unsatisfactory boundary artifacts, due to the …

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 …

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 …

Few-shot learning (FSL) aims to learn new categories with a few visual samples per class.
Few-shot class representations are often biased due to data scarcity. To mitigate this issue …

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 an important computer vision task, whose aim is to successfully detect
the shadow affected area appearing through light occlussion, followed by a photo-realistic …

Class-agnostic object counting aims to count object instances of an arbitrary class at test
time. It is challenging but also enables many potential applications. Current methods require …

Shadow removal is an important topic in image restoration, and it can benefit many
computer vision tasks. State-of-the-art shadow-removal methods typically employ deep …