CALL FOR PAPERS

Special Issue on Virtual Images for Visual Artificial Intelligence

Recently, deep learning has become one of the core technologies of computer vision and artificial intelligence. Deep learning is a data-driven technology and its performance heavily relies on large-scale labeled data, e.g., ImageNet and MS COCO. Unfortunately, it is rather expensive to collect and annotate large-scale image data from the real world, the collected real images are limited in covering complex environmental conditions, and the real scenes are uncontrollable and unrepeatable. As a result, the performance of current deep learning systems is not satisfactory while working in complex scenarios, such as autonomous driving and intelligent monitoring scenarios.

In light of the disadvantages of collecting images from the real world, more and more researchers start to synthesize and use virtual images for computer vision research. A variety of advanced techniques including computer graphics simulation, image style transfer, and generative models have been used for virtual image generation*. The virtual images are especially valuable to the training, testing, understanding and optimization of learning-based models in computer vision**.

This special issue emphasizes the important role of virtual images in deep learning and computer vision research, and welcomes a broad range of submissions developing and using virtual images for visual artificial intelligence. The list of possible topics includes, but is not limited to:

● New image synthesis methods

● Computer graphics and virtual/augmented reality for scene simulation

● Graphics rendering techniques for generating photorealistic virtual images

● Image-to-image translation and video-to-video translation

● Image super-resolution

● Deep generative models related to virtual images (variational autoencoders, generative adversarial networks, etc.)

● Neural networks that learn from virtual images

● Domain adaptation methods for deep learning

● Understanding deep architectures using virtual images

● Intelligent visual computing with virtual images

● Virtual-real interactive parallel vision and parallel imaging

● Virtual images and artistic creation

● Applications of virtual images to intelligent systems (robots, autonomous vehicles, visual monitoring systems, medical devices, and so on)

Authors should prepare their manuscripts according to the "Instructions for Authors" guidelines of “Neurocomputing” outlined at the journal website

https://www.elsevier.com/journals/neurocomputing/0925-2312/guide-for-authors.

All papers will be peer-reviewed following a regular reviewing procedure. Each submission should clearly demonstrate evidence of benefits to society or large communities. Originality and impact on society, in combination with a media-related focus and innovative technical aspects of the proposed solutions will be the major evaluation criteria.

* Kunfeng Wang, Chao Gou, and Fei-Yue Wang. Parallel vision: an ACP-based approach to intelligent vision computing. Acta Automatica Sinica, 2016, 42(10): 1490−1500.

** Kunfeng Wang, Chao Gou, Nanning Zheng, James M. Rehg, and Fei-Yue Wang. Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives. Artificial Intelligence Review, 2017, 48(3): 299−329.

Submission Deadline: April 15, 2018

First Review Decision: June 30, 2018

Revisions Due: July 31, 2018

Final Manuscript: September 30, 2018

Expected publication date: December 2018

Kunfeng Wang

Institute of Automation, Chinese Academy of Sciences, China and Qingdao Academy of Intelligent Industries, China

Managing Guest Editor

E-mail: kunfeng.wang@ia.ac.cn

Fei-Yue Wang

Institute of Automation, Chinese Academy of Sciences, China

E-mail: feiyue.wang@ia.ac.cn

Visvanathan Ramesh

Goethe University, Frankfurt, Germany

E-mail: ramesh@fias.uni-frankfurt.de

Ashish Shrivastava

Apple Inc., USA

E-mail: ashish.umd@gmail.com

David Vázquez

Autonomous University of Barcelona (UAB), Spain

E-mail: aklaway@gmail.com

Fuxin Li

Oregon State University, USA

E-mail: lif@engr.orst.edu