Autonomous vehicles (AVs) rely on various types of sensor technologies to perceive the
environment and to make logical decisions based on the gathered information similar to …

Recent progress on Transformers and multi-layer perceptron (MLP) models provide new
network architectural designs for computer vision tasks. Although these models proved to be …

Since convolutional neural networks (CNNs) perform well at learning generalizable image
priors from large-scale data, these models have been extensively applied to image …

Deep neural networks (DNN) have achieved great success in image restoration. However,
most DNN methods are designed as a black box, lacking transparency and interpretability …

Rain can cause performance degradation of outdoor computer vision tasks. Thus, the
exploration of rain removal from videos or a single image has drawn considerable attention …

In this paper, we explore the role of Instance Normalization in low-level vision tasks.
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to …

Image restoration tasks demand a complex balance between spatial details and high-level
contextualized information while recovering images. In this paper, we propose a novel …

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 …

Existing deep learning based de-raining approaches have resorted to the convolutional
architectures. However, the intrinsic limitations of convolution, including local receptive fields …

Image restoration under adverse weather conditions has been of significant interest for
various computer vision applications. Recent successful methods rely on the current …