Frontiers of Computer Science



Call for Papers

Special Section on

Deep Learning Applications in Computer Vision

Aim and Scope

Computer vision tries to acquire, process, analyze and understand visual data captured by all kinds of sensors from the real world. It is a crossing discipline between computer science and artificial intelligence, attracting a large population of researchers all over the world, especially from China.

Computer vision is also a booming field with various new approaches and theories proposed every year. Deep learning, which can be treated as the most significant breakthrough in the past 10 years, has greatly affected the methodology of computer vision and achieved terrific progress in both academy and industry. Deep learning is firstly adopted in ImageNet Competition for object categorization, which achieved a 12% progress in 2012 and confirmed the priority of deep learning for computer vision applications. From then on, deep learning has been adopted in all kinds of computer vision applications and many breakthroughs have achieved in sub-areas, like DeepFace on LFW competition for face verification, GoogleNet for ImageNet Competition for object categorization. It can be expected that more and more computer vision applications will benefit from Deep learning.

This special section mainly focuses on Deep Learning applications in computer vision. We are soliciting original contributions, of leading researchers and practitioners from academia as well as industry, which address a wide range of theoretical and application issues in deep learning for vision. Original papers to survey the recent progress in this exciting area and highlight potential solutions to common challenging problems is also welcome. The topics include, but not limited to:

l  Deep neural network design for specific vision applications
l  Optimization for deep learning
l  Supervised deep learing
l  Unsupervised deep learning
l  Sparse coding in deep learning
l  Transfer learning for deep learning
l  Deep learning for feature representation
l  Deep learning for face analysis
l  Deep learning for object recognition
l  Deep learning for scene understanding
l  Deep learning for text recognition
l  Deep learning theory
l  Deep learning for dimension reduction
l  Deep learning for activity recognition
l  Deep learning for biometrics
l  Performance evaluation of deep learning

Important Dates

Full paper due: February 1st, 2016
First notification: May 1st, 2016
Revised manuscript: June 1st, 2016
Acceptance notification: September 1st, 2016
Final manuscript due: October 1st, 2016
Publication of the special section (expected, flexible): February1st , 2017

Guest Editors

Zhaoxiang Zhang, Institute of Automation, Chinese Academy of Sciences
Rongrong Ji, Xiamen University
Xiang Bai, Huazhong University of Science and Technology
Shiguang Shan, Institute of Computing Technology,ChineseAcademyof Sciences

Submission Online