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 …
Y Dong, Q Liu, B Du, L Zhang - IEEE Transactions on Image …, 2022 - ieeexplore.ieee.org
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 …
J Kwon, J Kim, H Park, IK Choi - International Conference on …, 2021 - proceedings.mlr.press
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 …
SK Roy, S Manna, T Song… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
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 …
S Cong, Y Zhou - Artificial Intelligence Review, 2023 - Springer
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 …