Artificial neural networks and deep learning in the visual arts: A review
In this article, we perform an exhaustive analysis of the use of Artificial Neural Networks and
Deep Learning in the Visual Arts. We begin by introducing changes in Artificial Intelligence …
Deep Learning in the Visual Arts. We begin by introducing changes in Artificial Intelligence …
Benign and malignant classification of mammogram images based on deep learning
H Li, S Zhuang, D Li, J Zhao, Y Ma - Biomedical Signal Processing and …, 2019 - Elsevier
Breast cancer is one of the most common malignant tumors in women, which seriously affect
women's physical and mental health and even threat to life. At present, mammography is an …
women's physical and mental health and even threat to life. At present, mammography is an …
GSCCTL: a general semi-supervised scene classification method for remote sensing images based on clustering and transfer learning
H Song, W Yang - International Journal of Remote Sensing, 2022 - Taylor & Francis
Recently, much research has shown that deep learning methods are superior in scene
classification for remote sensing images (HSIs). However, the lack of labelled samples and …
classification for remote sensing images (HSIs). However, the lack of labelled samples and …
Advances and challenges in computational image aesthetics
Computational image aesthetics aims at designing algorithmic approaches to perform
aesthetic decisions, in a similar fashion as humans. In the past fifteen years, computational …
aesthetic decisions, in a similar fashion as humans. In the past fifteen years, computational …
A novel network level fusion architecture of proposed self-attention and vision transformer models for land use and land cover classification from remote sensing …
Convolutional neural networks (CNNs), in particular, demonstrate the remarkable power of
feature learning in remote sensing for land use and cover classification, as demonstrated by …
feature learning in remote sensing for land use and cover classification, as demonstrated by …
Multi-level transitional contrast learning for personalized image aesthetics assessment
Personalized image aesthetics assessment (PIAA) is aimed at modeling the unique
aesthetic preferences of individuals, based on which personalized aesthetic scores are …
aesthetic preferences of individuals, based on which personalized aesthetic scores are …
[HTML][HTML] Mining security assessment in an underground environment using a novel face recognition method with improved multiscale neural network
Overstaffing production in underground coal mining is not convenient for daily management,
and incomplete information of coal miners hinders the rescue process of firefighters during …
and incomplete information of coal miners hinders the rescue process of firefighters during …
Predicting aesthetic score distribution through cumulative jensen-shannon divergence
Aesthetic quality prediction is a challenging task in the computer vision community because
of the complex interplay with semantic contents and photographic technologies. Recent …
of the complex interplay with semantic contents and photographic technologies. Recent …
Automated assessment of student hand drawings in free-response items on the particulate nature of matter
Here, we describe the development and validation of an automatic assessment system that
examines students' hand-drawn visual representations in free-response items. The data …
examines students' hand-drawn visual representations in free-response items. The data …
ILGNet: Inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation
In this study, the authors address a challenging problem of aesthetic image classification,
which is to label an input image as high‐or low‐aesthetic quality. We take both the local and …
which is to label an input image as high‐or low‐aesthetic quality. We take both the local and …