Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …

Deep neural networks for computer vision are deployed in increasingly safety-critical and
socially-impactful applications, motivating the need to close the gap in model performance …

While generative adversarial networks (GANs) can successfully produce high-quality
images, they can be challenging to control. Simplifying GAN-based image generation is …

Semantic segmentation usually suffers from a long tail data distribution. Due to the
imbalanced number of samples across categories, the features of those tail classes may get …

The success of image perturbations that are designed to fool image classifier is assessed in
terms of both adversarial effect and visual imperceptibility. The conventional assumption on …

Image harmonization, aiming to make composite images look more realistic, is an important
and challenging task. The composite, synthesized by combining foreground from one image …

We introduce a deep learning approach to realistically edit an sRGB image's white balance.
Cameras capture sensor images that are rendered by their integrated signal processor (ISP) …

We explore the zero-shot setting for day-night domain adaptation. The traditional domain
adaptation setting is to train on one domain and adapt to the target domain by exploiting …

In this paper, we present CLCC, a novel contrastive learning framework for color constancy.
Contrastive learning has been applied for learning high-quality visual representations for …

Image enhancement is an essential technique used in many imaging applications. The main
motivation of image enhancement is processing an image to be more suitable for specific …