Home / Journals Journal of Electrical and Electronic Engineering / Learning from Weakly or Webly Supervised Data
Learning from Weakly or Webly Supervised Data
Submission DeadlineJan. 15, 2020

Submission Guidelines: http://www.sciencepublishinggroup.com/home/submission

Lead Guest Editor
Yazhou Yao
Computer Vision Research Group, Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates
Guest Editors
  • Fumin Shen
    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
  • Jian Zhang
    Global Big Data Technologies Center, University of Technology Sydney, Sydney, Australia
  • Jun Li
    Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, USA
  • Fengchao Xiong
    College of Computer Science, Zhejiang University, Hangzhou, China
  • Xiangbo Shu
    School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
  • Jingsong Xu
    Global Big Data Technologies Center, University of Technology Sydney, Sydney, Australia
  • Fang Zhao
    Computer Vision Research Group, Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates
  • Guosen Xie
    Computer Vision Research Group, Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates
  • Lizhong Ding
    Computer Vision Research Group, Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates
  • Computer Vision Research Group, Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates
  • Mamta Yadav
    Department of Computing Sciences, Texas A&M University-Corpus Christi, Corpus Christi, Texas, USA
  • Elemasetty Uday Kiran
    Department of Electrical and Electronics,Space Development Nexus,Center for Isro Gnss Studies, Hyderabad, Telangana, India
  • Sankhanil Dey
    Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, West Bengal, India
Introduction
In the past few years, labeled image datasets have played a critical role in high-level image understanding. For example, ImageNet has acted as one of the most important factors in the recent advance of developing and deploying visual representation learning models. However, the process of constructing ImageNet is both time-consuming and labor-intensive. To reduce the time and labor costs of manual annotation, some works also focused on weakly supervised learning. To further reduce the cost of manual annotation, learning directly from the web data has attracted more and more people's attention. Compared to manual-labeled image datasets, web images are a rich and free resource. For arbitrary categories, the potential training data can be easily obtained from the image search engines like Google or Bing. Unfortunately, due to the error index of the image search engine, the precision of returned images from an image search engine is still unsatisfactory. Original research papers are solicited in any aspect of weakly supervised or webly-supervised learning are welcome.
Aims and Scope:
  1. Weakly supervised learning
  2. Webly supervised learning
  3. Image classification
  4. Object detection
  5. Deep convolutional neural networks
  6. Clustering based methods
Guidelines for Submission
Manuscripts should be formatted according to the guidelines for authors
(see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=239).

Please download the template to format your manuscript.

ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186