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Submit a Manuscript to the Journal
Connection Science

For a Special Issue on
Efficient Deep Neural Networks for Image Processing in End Side Devices

Manuscript deadline
31 May 2022

Cover image - Connection Science

Special Issue Editor(s)

Xiao Bai, School of Computer Science and Engineer, Beihang University, China
[email protected]

Xin Ning, Institute of Semiconductors, Chinese Academy of Sciences,China.
[email protected]

Jun Zhou, School of Information and Communication Technology, Griffith University, Australia
[email protected]

Jing Wu, School of Computer Science and Informatics, Cardiff University, Cardiff
[email protected]

Chen Wang, School of Computer Science and Engineering, State Key Laboratory of Software Development Environment, Jiangxi Research Institute, Beihang University, China.
[email protected]

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Efficient Deep Neural Networks for Image Processing in End Side Devices

Robots, unmanned aerial vehicles, intelligent terminals in the home, smart phones, tablet computers, and other end side devices demand efficient image processing algorithms. Deep neural networks are widely used in computer vision tasks such as image classification and object detection, and they have made impressive improvements over the last few years. Owing to their huge commercial value, deep learning and convolutional neural networks have become research hotspots, and numerous excellent work have been conducted. At present, traditional deep neural networks are designed to extract more expressive depth features via a very deep neural network structure. This has presented a huge challenge to the deployment of convolution neural networks on various hardware platforms, especially mobile and edge devices, and has severely limited the development and application of deep neural networks on portable devices. The key to improving the efficiency and ability of mobile terminals to process image and video data, and to meeting the constraints of storage space and power consumption, lies in the lightweight design, model compression, and acceleration of deep neural networks, and this has been highlighted by academia and industry.

This special issue aims to gather works of state-of-the-art research in the field of computer vision and promote the deployment and implementation of lightweight deep neural network models in edge devices. We invite authors to submit manuscripts that are highly relevant to the topics of this special issue and that have not been published before.

The topics of interest include, but are not limited to:

  • Image processing based on deep neural networks
  • Pattern recognition based on deep neural networks
  • Visual navigation based on deep neural networks
  • Lightweight deep neural network structure design
  • Parameter pruning and sharing in deep neural networks
  • Pruning and thinning in deep neural networks
  • Quantification of deep neural networks
  • Knowledge distillation of deep neural networks
  • Neural Architecture Search and AutoML

Submission Instructions

- Click on the 'Submit an Article' button below

- When submitting your paper, answer 'yes' to the question 'Are you submitting your paper for a specific special issue?'

- Type "Efficient Deep Neural Networks for Image Processing in End Side Devicesโ€ in the free text box

- Papers with technical contributions will be mainly considered but survey papers may be considered only if of sufficient merit and that strictly adhere to the theme of the special issue

Instructions for AuthorsSubmit an Article