The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

Camouflaged object detection (COD) aims to address the tough issue of identifying
camouflaged objects visually blended into the surrounding backgrounds. COD is a …

Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in
unconditional image generation. However, due to the stochasticity of the generative process …

Can a generative model be trained to produce images from a specific domain, guided only
by a text prompt, without seeing any image? In other words: can an image generator be …

Existing neural style transfer methods require reference style images to transfer texture
information of style images to content images. However, in many practical situations, users …

Diffusion models are rising as a powerful solution for high-fidelity image generation, which
exceeds GANs in quality in many circumstances. However, their slow training and inference …

In image-to-image translation, each patch in the output should reflect the content of the
corresponding patch in the input, independent of domain. We propose a straightforward …

Abstract 3D scene stylization aims at generating stylized images of the scene from arbitrary
novel views following a given set of style examples, while ensuring consistency when …

Generative adversarial networks (GANs) have recently found applications in image editing.
However, most GAN-based image editing methods often require large-scale datasets with …

Universal style transfer retains styles from reference images in content images. While
existing methods have achieved state-of-the-art style transfer performance, they are not …