High-resolution representations are essential for position-sensitive vision problems, such as
human pose estimation, semantic segmentation, and object detection. Existing state-of-the …

The breakthrough brought by generative adversarial networks (GANs) in computer vision
(CV) applications has gained a lot of attention in different fields due to their ability to capture …

Recent advances in 3D scene representation and novel view synthesis have witnessed the
rise of Neural Radiance Fields (NeRFs). Nevertheless, it is not trivial to exploit NeRF for the …

With the development of facial recognition technology, face anti-spoofing as the most
important security module of face recognition system becomes more and more important. As …

We introduce an effective strategy to generate an annotated synthetic dataset of
microbiological images of Petri dishes that can be used to train deep learning models in a …

Photography, like painting, allows artists to express themselves through their unique style. In
digital photography, this is achieved not only with the choice of the subject and the …

We propose a photorealistic style transfer network to emphasize the natural effect of
photorealistic image stylization. In general, distortion of the image content and lacking of …

Deep learning-based information processing consumes long time and requires huge
computing resources, especially for dense prediction tasks which require an output for each …

ABSTRACT Neural Style Transfer (NST) has been a widely researched topic as of late
enabling new forms of image manipulation. Here we perform an extensive study on NST …

Unsupervised domain adaptation (UDA) becomes more and more popular in tackling real-
world problems without ground truths of the target domain. Though tedious annotation work …