M Hnewa, H Radha - IEEE Signal Processing Magazine, 2020 - ieeexplore.ieee.org
Advanced automotive active safety systems, in general, and autonomous vehicles, in particular, rely heavily on visual data to classify and localize objects, such as pedestrians …
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 …
C Mou, Q Wang, J Zhang - … of the IEEE/CVF Conference on …, 2022 - openaccess.thecvf.com
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 …
X Chen, H Li, M Li, J Pan - … of the IEEE/CVF Conference on …, 2023 - openaccess.thecvf.com
Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction …
Y Yang, C Wang, R Liu, L Zhang… - Proceedings of the …, 2022 - openaccess.thecvf.com
To overcome the overfitting issue of dehazing models trained on synthetic hazy-clean image pairs, many recent methods attempted to improve models' generalization ability by training …
L Chen, X Lu, J Zhang, X Chu… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
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 …