Frontier of Computer Science: Call for Papers Special Section on Deep Learning A
Call for Papers Special Section on Deep Learning Applications in Computer Vision
http://journal.hep.com.cn/fcs/EN/column/item570.shtml
Aim and ScopeComputer 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:lDeep neural network design for specific vision applicationslOptimization for deep learninglSupervised deep learinglUnsupervised deep learninglSparse coding in deep learninglTransfer learning for deep learninglDeep learning for feature representationlDeep learning for face analysislDeep learning for object recognitionlDeep learning for scene understandinglDeep learning for text recognitionlDeep learning theorylDeep learning for dimension reductionlDeep learning for activity recognitionlDeep learning for biometricslPerformance evaluation of deep learningImportant DatesFull paper due: February 1st, 2016First notification: May 1st, 2016Revised manuscript: June 1st, 2016Acceptance notification: September 1st, 2016Final manuscript due: October 1st, 2016Publication of the special section (expected, flexible): February1st , 2017Guest EditorsZhaoxiang Zhang, Institute of Automation, Chinese Academy of SciencesRongrong Ji, Xiamen UniversityXiang Bai, Huazhong University of Science and TechnologyShiguang Shan, Institute of Computing Technology,ChineseAcademyof SciencesSubmission Onlinehttp://mc.manuscriptcentral.com/hepfcs
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