Object detection or localization is an incremental step in progression from coarse to fine
digital image inference. It not only provides the classes of the image objects, but also …

Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

As the computing power of modern hardware is increasing strongly, pre-trained deep
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …

Blind face restoration usually relies on facial priors, such as facial geometry prior or
reference prior, to restore realistic and faithful details. However, very low-quality inputs …

Recent works on plug-and-play image restoration have shown that a denoiser can implicitly
serve as the image prior for model-based methods to solve many inverse problems. Such a …

Learning-based single image super-resolution (SISR) methods are continuously showing
superior effectiveness and efficiency over traditional model-based methods, largely due to …

Deep convolutional neural networks perform better on images containing spatially invariant
noise (synthetic noise); however, its performance is limited on real-noisy photographs and …

For person re-identification (re-id), attention mechanisms have become attractive as they
aim at strengthening discriminative features and suppressing irrelevant ones, which …

Deep convolutional networks have become a popular tool for image generation and
restoration. Generally, their excellent performance is imputed to their ability to learn realistic …

Both convolutional and recurrent operations are building blocks that process one local
neighborhood at a time. In this paper, we present non-local operations as a generic family of …