Results 21 to 30 of about 203,239 (310)

Performance analysis of different DCNN models in remote sensing image object detection

EURASIP Journal on Image and Video Processing, 2022
In recent years, deep learning, especially deep convolutional neural networks (DCNN), has made great progress. Many researchers use different DCNN models to detect remote sensing targets. Different DCNN models have different advantages and disadvantages.
Huaijin Liu, Jixiang Du, Yong Zhang, Hongbo Zhang   +3 more
doaj   +1 more source

Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]

Jisuanji kexue
Node classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node ...
ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo
doaj   +1 more source

Convolutional neural networks in APL [PDF]

Proceedings of the 6th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming, 2019
This paper shows how a Convolutional Neural Network (CNN) can be implemented in APL. Its first-class array support ideally fits that domain, and the operations of APL facilitate rapid and concise creation of generically reusable building blocks. For our example, only ten blocks are needed, and they can be expressed as ten lines of native APL. All these
Artjoms Sinkarovs, Robert Bernecky, Sven-Bodo Scholz   +2 more
openaire   +1 more source

Cloud-based video analytics using convolutional neural networks. [PDF]

, 2018
Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management.
Anjum, Ashiq, Yaseen, M., Antonopoulos, Nick, Farid, Mohsen   +3 more
core   +1 more source

A Hybrid Framework for Visual Positioning: Combining Convolutional Neural Networks with Ontologies

EAI Endorsed Transactions on Energy Web, 2022
Visual positioning is a new generation positioning technique which has been developed rapidly during recent years for many applications such as robotics, self-driving vehicles and positioning for visually impaired people due to advent of powerful image
Abdolreza Mosaddegh, Sérgio Lopes, Habib Rostami, Ahmad Keshavarz, Sara Paiva   +4 more
doaj   +1 more source

FocusedDropout for Convolutional Neural Network

CoRR, 2021
In convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially. Except randomly discarding regions or channels, many approaches try to overcome this defect by dropping influential units.
Tianshu Xie, Minghui Liu, Jiali Deng, Xuan Cheng, Xiaomin Wang, Ming Liu 0002   +5 more
openaire   +2 more sources

An Attention Module for Convolutional Neural Networks

, 2021
Attention mechanism has been regarded as an advanced technique to capture long-range feature interactions and to boost the representation capability for convolutional neural networks.
Zhu, B. (author), Lee, Jinho (author), Hofstee, H.P. (author), Al-Ars, Z. (author)   +3 more
core   +1 more source

A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification

Sensors, 2019
This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to ...
Lu Han, Chongchong Yu, Kaitai Xiao, Xia Zhao   +3 more
doaj   +1 more source

Optimization design of binary VGG convolutional neural network accelerator

Dianzi Jishu Yingyong, 2021
Most of the existing researches on accelerators of binary convolutional neural networks based on FPGA are aimed at small-scale image input, while the applications mainly take large-scale convolutional neural networks such as YOLO and VGG as backbone ...
Zhang Xuxin, Zhang Jia, Li Xinzeng, Jin Jie   +3 more
doaj   +1 more source

Interpretable Convolutional Neural Networks [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
This paper proposes a method to modify traditional convolutional neural networks (CNNs) into interpretable CNNs, in order to clarify knowledge representations in high conv-layers of CNNs. In an interpretable CNN, each filter in a high conv-layer represents a certain object part.
Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu   +2 more
openaire   +2 more sources