Several image processing tasks, such as image classification and object detection, have
been significantly improved using Convolutional Neural Networks (CNN). Like ResNet and …

In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), such as Graph
Attention Networks (GAT), are two classic neural network models, which are applied to the …

Abstract Vision Transformer (ViT) extends the application range of transformers from
language processing to computer vision tasks as being an alternative architecture against …

Feature fusion, the combination of features from different layers or branches, is an
omnipresent part of modern network architectures. It is often implemented via simple …

Recently, learning algorithms motivated from sharpness of loss surface as an effective
measure of generalization gap have shown state-of-the-art performances. Nevertheless …

Neural networks have enabled state-of-the-art approaches to achieve incredible results on
computer vision tasks such as object detection. However, such success greatly relies on …

Hyperspectral images (HSIs) provide rich spectral–spatial information with stacked
hundreds of contiguous narrowbands. Due to the existence of noise and band correlation …

Recent work on automated augmentation strategies has led to state-of-the-art results in
image classification and object detection. An obstacle to a large-scale adoption of these …

The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …