Results 31 to 40 of about 1,532,152 (301)

BA-Net: Bridge Attention for Deep Convolutional Neural Networks

, 2022
In recent years, channel attention mechanism has been widely investigated due to its great potential in improving the performance of deep convolutional neural networks (CNNs) in many vision tasks. However, in most of the existing methods, only the output of the adjacent convolution layer is fed into the attention layer for calculating the channel ...
Yue Zhao 0040, Junzhou Chen 0001, Zirui Zhang, Ronghui Zhang   +3 more
openaire   +2 more sources

DETECTION OF CLOUDS IN MEDIUM-RESOLUTION SATELLITE IMAGERY USING DEEP CONVOLUTIONAL NEURAL NETS [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022
Cloud detection is an inextricable pre-processing step in remote sensing image analysis workflows. Most of the traditional rule-based and machine-learning-based algorithms utilize low-level features of the clouds and classify individual cloud pixels ...
A. Hasan, C. Witharana, M. R. Udawalpola, A. K. Liljedahl   +3 more
doaj   +1 more source

2.5D MFFAU-Net: a convolutional neural network for kidney segmentation

BMC Medical Informatics and Decision Making, 2023
AbstractBackgroundKidney tumors have become increasingly prevalent among adults and are now considered one of the most common types of tumors. Accurate segmentation of kidney tumors can help physicians assess tumor complexity and aggressiveness before surgery.
Peng Sun, Zengnan Mo, Fangrong Hu, Xin Song, Taiping Mo, Bonan Yu, Yewei Zhang, Zhencheng Chen   +7 more
openaire   +3 more sources

Brain inspired neuronal silencing mechanism to enable reliable sequence identification

Scientific Reports, 2022
Real-time sequence identification is a core use-case of artificial neural networks (ANNs), ranging from recognizing temporal events to identifying verification codes.
Shiri Hodassman, Yuval Meir, Karin Kisos, Itamar Ben-Noam, Yael Tugendhaft, Amir Goldental, Roni Vardi, Ido Kanter   +7 more
doaj   +1 more source

A deep LSTM‐CNN based on self‐attention mechanism with input data reduction for short‐term load forecasting

IET Generation, Transmission & Distribution, 2023
Numerous studies on short‐term load forecasting (STLF) have used feature extraction methods to increase the model's accuracy by incorporating multidimensional features containing time, weather and distance information.
Shiyan Yi, Haichun Liu, Tao Chen, Jianwen Zhang, Yibo Fan   +4 more
doaj   +1 more source

Bayesian Topological Convolutional Neural Nets

CoRR
Convolutional neural networks (CNNs) have been established as the main workhorse in image data processing; nonetheless, they require large amounts of data to train, often produce overconfident predictions, and frequently lack the ability to quantify the uncertainty of their predictions.
Sarah Harkins Dayton, Hayden Everett, Ioannis D. Schizas, David L. Boothe, Vasileios Maroulas   +4 more
openaire   +2 more sources

Novel multi‐domain attention for abstractive summarisation

CAAI Transactions on Intelligence Technology, 2023
The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary, and the summary generated by models lacks the cover of the subject of source document due to models ...
Chunxia Qu, Ling Lu, Aijuan Wang, Wu Yang, Yinong Chen   +4 more
doaj   +1 more source

SF-ICNN: Spectral–Fractal Iterative Convolutional Neural Network for Classification of Hyperspectral Images [PDF]

One primary concern in the field of remote-sensing image processing is the precise classification of hyperspectral images (HSIs). Lately, deep-learning models have demonstrated cutting-edge results in HSI classification.
Akbari, Vahid, Behnam Asghari Beirami, Mehran Alizadeh Pirbasti, Alizadeh Pirbasti, Mehran, Asghari Beirami, Behnam, Vahid Akbari   +5 more
core   +1 more source

End‐to‐end global to local convolutional neural network learning for hand pose recovery in depth data

IET Computer Vision, 2022
Despite recent advances in 3‐D pose estimation of human hands, thanks to the advent of convolutional neural networks (CNNs) and depth cameras, this task is still far from being solved in uncontrolled setups.
Meysam Madadi, Sergio Escalera, Xavier Baró, Jordi Gonzàlez   +3 more
doaj   +1 more source

Network Inversion of Convolutional Neural Nets

CoRR
Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability and reliability, especially in safety-critical scenarios.
Pirzada Suhail, Amit Sethi
openaire   +3 more sources