Recently, open-vocabulary learning has emerged to accomplish segmentation for arbitrary
categories of text-based descriptions, which popularizes the segmentation system to more …

Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual
apps spanning from on-demand movies and 360° videos to video-conferencing and live …

Image-level weakly supervised semantic segmentation (WSSS) is a fundamental yet
challenging computer vision task facilitating scene understanding and automatic driving …

This paper studies the single image super-resolution problem using adder neural networks
(AdderNets). Compared with convolutional neural networks, AdderNets utilize additions to …

Despite breakthrough advances in image super-resolution (SR) with convolutional neural
networks (CNNs), SR has yet to enjoy ubiquitous applications due to the high computational …

Abstract Single-Image Super Resolution (SISR) is a classical computer vision problem and it
has been studied for over decades. With the recent success of deep learning methods …

Neural network quantization aims at reducing bit-widths of weights and activations for
memory and computational efficiency. Since a linear quantizer (ie, round (*) function) cannot …

Deep learning has made unprecedented progress in image restoration (IR), where residual
block (RB) is popularly used and has a significant effect on promising performance …

Abstract Generative Adversarial Networks (GANs) have witnessed prevailing success in
yielding outstanding images, however, they are burdensome to deploy on resource …

A number of super-resolution (SR) algorithms from in terpolation to deep neural networks
(DNN) have emerged to restore or create missing details of the input low-resolution image …