[Ieee_vis] IEEE CG&A Special Issue on Visual Computing with Deep Learning - Call for Papers

[Shixia Liu]

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IEEE CG&A Special Issue on Visual Computing with Deep Learning – Call for
Papers

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The great success of deep learning techniques in computer vision, speech
recognition, and natural language processing has recently attracted much
attention. While machine learning techniques have long been used to solve a
wide range of graphics and visualization problems, most of them rely on
problem-specific “feature engineering” to extract favorable features from
the training data, which is often a manually-tweaked, time-consuming
process and usually does not generalize well. Deep learning techniques, on
the other hand, are capable of automatically discovering features
appropriate for a specific task from raw data, which reduces the need for
feature engineering and makes it easier to develop end-to-end solutions.
The recent advances in Generative Adversarial Networks (GAN) and
reinforcement learning methods show their potential for data generation and
action planning. It is expected that the use of deep learning techniques
can significantly advance the performance of many state-of-the-art graphics
and visualization algorithms.



Unlike computer vision applications, which mainly focus on visual content
analysis and understanding, graphics and visualization tasks must often
create visual content (e.g., synthesizing an image, generating an animation
sequence, visualizing and interpreting spatial-temporal data) that exhibits
the high quality to be used in entertainment or visualization applications.
Furthermore, end-to-end deep learning techniques require a large amount of
labelled data to work optimally. This raises an additional challenge
because, unlike computer vision, which relies on natural images or video
that can be conveniently collected on Internet, high-quality synthesized
visual content with proper labeling is rare. Finally, different from many
vision tasks where automation is the ultimate goal, creating visual content
is often an interactive, progressive process. Therefore, user interaction
must be integrated into the learning and run-time computation process.



For this special issue, we are soliciting papers that describe algorithms,
data structures, tools and systems that use deep learning or facilitate the
use of deep learning for graphics and visualization tasks. More
specifically, we are looking for contributions that demonstrate practical
impact of deep learning on (but not limited to) the following topics:

- Visual analytics applications

- Object/scene reconstruction from RGB/RGBD images

- Shape analysis and synthesis

- Appearance capture and modeling

- Global illumination and real-time rendering

- Sound synthesis and rendering

- Physics-based simulation of fluids and deformable objects

- Performance-based face/body animation

- Computational photography

- Deep learning models and training schemes for visual content creation



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Important Date:

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Final submissions due: 1 July 2018

Publication date: March/April 2019



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Guest Editors

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Kun Zhou, kunzhou at zju.edu.cn

Xin Tong, Microsoft Research Asia, xtong at microsoft.com



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Submission Guidelines

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Non department articles submitted to IEEE CG&A should not exceed 8,000
words, including the main text, abstract, keywords, bibliography,
biographies, and table text, where a page is approximately 800 words.
Articles should include no more than 10 figures or images. Each 1/4 page
figure, image, and table counts for approx. 200 words. Note that all
tables, images, and illustrations must be appropriately scaled and legible;
larger elements should be accounted for accordingly with respect to word
count. Please limit the number of references to the most relevant and
ensure to delineate your work from relevant past articles in CG&A.
Furthermore, avoid an excessive number of references to published work that
might only be marginally relevant. Consider instead providing such
pertinent background material in sidebars for non-expert readers. Visit the
CG&A style and length guidelines at
www.computer.org/web/peer-review/magazines. We also strongly encourage you
to submit multimedia (videos, podcasts, and so on) to enhance your article.
Visit the CG&A supplemental guidelines at
www.computer.org/web/peer-review/magazines.



Please submit your paper using the online manuscript submission service at
https://mc.manuscriptcentral.com/cs-ieee. When uploading your paper, select
the appropriate special issue title under the category “Manuscript Type.”
Also, include complete contact information for all authors. If you have any
questions about submitting your article, contact the peer review
coordinator at cga-ma at computer.org.



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CFP Web Page:

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https://publications.computer.org/cga/2017/12/09/special-issue-visual-computing-deep-learning-call-papers/
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