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Mobile Networks and Applications

The Journal of SPECIAL ISSUES on Mobility of Systems, Users, Data and Computing

Publishing model:
Hybrid

Mobile Networks and Applications - Resource Efficient Deep Learning for Computer Vision Applications

Despite the success of deep learning models in many areas, they have been emerging at the cost of increasingly large scale of models, which require powerful computing and data resources. However, as a common situation in practical computer vision applications, especially on the edge side such as mobile applications, resource limitations are something that must be considered, such as limited computing power, high real-time requirement, and insufficient data. Thus, resource-constrained deep learning theories, methods and applications should receive enough attention. There exist several different directions towards making deep learning efficient for computer vision, thereby leading to a reduction in the required data set size, computational memory or the associated training and inference time. Most research that exists focuses on making deep learning methods efficient, however, the resource associated with real-world mobile devices can vary drastically, and a method termed efficient for a certain choice of resource budgets might be completely inefficient for a different one. This special issue focuses on budget-aware model training and inference, thereby maximally utilizing the available resources of data and computing.

Topics: 

  • Efficient neural architectures design or search for downstream computer vision applications  
  • Efficient training and inference of models under different computing resource constraints  
  • Structured and unstructured extreme compression methods of existing models 
  • Novel architectures for processing and training very large images under resource constraints  
  • Efficient fine-tuning of large computer vision models in various applications  
  • Benchmarking datasets for model efficiency in computer vision applications  
  • Data quality assessment towards data efficient deep learning for computer vision tasks  
  • Eco-friendly low-carbon technology and innovative solutions for computer vision tasks

Important Dates: 

  • Manuscript submission deadline: 30 October 2024
  • Notification of acceptance: 30 November 2024
  • Submission of final revised paper: 30 December 2024
  • Publication of special issue (tentative): Dec 2024

Submission Procedure:


Authors should follow the MONET Journal manuscript format described at the journal site. Manuscripts should be submitted on-line through http://www.editorialmanager.com/mone/. A copy of the manuscript should also be emailed to the Guest Editors at the following email address liyang328@shzu.edu.cn


Guest Editors:


Dr. Yang Li (Managing Guest Editor), Shihezi University, China. Email: liyang328@shzu.edu.cn 

Dr. Houbing Herbert Song, University of Maryland, Baltimore County (UMBC), USA. Email: h.song@ieee.org


Guest Editors’ Information:

Dr. Yang Li is an Associate Editor for journals “Precision Agriculture”, “Plant Methods”, and “Data Technologies and Applications”. Currently, he is an Associate Professor with the College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China. His recent research interests include machine learning, computer vision, information processing, IoT, data quality assessment. He has published over 60 research papers in the international reputed journals, such as IEEE TII, IoTJ, JSTARS, TNSE, TASE, TCSVT, IS. His research has been funded by the National Natural Science Foundation of China. Google Scholar: https://scholar.google.com.hk/citations?user=Jz08dRMAAAAJ


Dr. Houbing Herbert Song is an IEEE Fellow, also acts as an Associate Editor for journals “IEEE Internet of Things Journal”, “IEEE Transactions on Intelligent Transportation Systems”, “IEEE Communications Magazine”, “IEEE Access”, etc. Currently, he is an Associate Professor with the Department of Information Systems, University of Maryland, Baltimore County (UMBC), USA. He is the Director of Security and Optimization for Networked Globe Laboratory (SONG Lab, www.SONGLab.us). He has published more than 300 technical articles. His recent research interests include cyber-physical systems, cybersecurity and privacy, internet of things, edge computing, unmanned aircraft systems, smart health, and wireless communications and networking. Google Scholar: https://scholar.google.com/citations?user=iJ_XxxoAAAAJ

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