Coming Special Issue
Expiring Date:
Jul. 20, 2019
Submit a Manuscript
share
Special Issues
Expand the Popularity of Your Conference
Publish conference papers as a Special Issue
Send your Special Issue proposal to:
special.issues@sciencepublishinggroup.com
Submit Hot Topics
Submit
If you wish to order hard copies, please click here to know more information.
Home / Journals / Journal of Electrical and Electronic Engineering / Learning from Weakly or Webly Supervised Data
Learning from Weakly or Webly Supervised Data
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
Tianfei Zhou
Computer Vision Research Group, Inception Institute of Artificial Intelligence
Abu Dhabi, United Arab Emirates
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
ADDRESS
Science Publishing Group
548 FASHION AVENUE
NEW YORK, NY 10018
U.S.A.
Tel: (001)347-983-5186