Results 21 to 30 of about 52,973 (279)

High‐precision skeleton‐based human repetitive action counting

IET Computer Vision, 2023
A novel counting model is presented by the authors to estimate the number of repetitive actions in temporal 3D skeleton data. As per the authors’ knowledge, this is the first work of this kind using skeleton data for high‐precision repetitive action ...
Chengxian Li, Ming Shao, Qirui Yang, Siyu Xia   +3 more
doaj   +1 more source

Vehicle Re-identification method based on Swin-Transformer network

Array, 2022
Vehicle Re-identification is to find out the exact vehicle captured by other cameras given a vehicle image. In natural traffic surveillance systems, vehicle re-identification can play a role in target vehicles localization, supervision, and criminal ...
Jianrong Li, Chang Yu, Jinyuan Shi, Chuanlei Zhang, Ting Ke   +4 more
doaj   +1 more source

C-Net: A reliable convolutional neural network for biomedical image classification [PDF]

Expert Systems with Applications, 2022
Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities.
Hosein Barzekar, Zeyun Yu
openaire   +2 more sources

Multi‐objective single‐shot neural architecture search via efficient convolutional filters

Electronics Letters, 2023
This paper presents a novel approach for fast neural architecture search (NAS) in Convolutional Neural Networks (CNNs) for end‐to‐end License Plate Recognition (LPR).
Seyed Mahdi Shariatzadeh, Mahmood Fathy, Reza Berangi   +2 more
doaj   +1 more source

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

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

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

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

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

Convolutional deep rectifier neural nets for phone recognition [PDF]

Interspeech 2013, 2013
Rectifier neurons differ from standard ones only in that the sigmoid activation function is replaced by the rectifier function, max(0, x). Several recent studies suggest that rectifier units may be more suitable building units for deep nets. For example, we found that with deep rectifier networks one can attain a similar speech recognition performance ...
openaire   +1 more source