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 …

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 …

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 …

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 …

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 …

Abstract We propose Deep Feature Factorization (DFF), a method capable of localizing
similar semantic concepts within an image or a set of images. We use DFF to gain insight …

Two-stage object detectors generate object proposals and classify them to detect objects in
images. These proposals often do not perfectly contain the objects but overlap with them in …

Incorporating pixel contextual information is critical for accurate segmentation. In this paper,
we show that an effective way to incorporate contextual information is through a patch-based …

Our paper introduces a novel two-stage self-supervised approach for detecting co-occurring
salient objects (CoSOD) in image groups without requiring segmentation annotations …