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<div class="moz-forward-container">FYI:<br>
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-------- Forwarded Message --------
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<th nowrap="nowrap" valign="BASELINE" align="RIGHT">Subject:
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<td>[Ieee_vis] IEEE CG&A Special Issue on Visual
Computing with Deep Learning - Call for Papers</td>
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<th nowrap="nowrap" valign="BASELINE" align="RIGHT">Date: </th>
<td>Fri, 1 Jun 2018 20:56:30 +0800</td>
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<th nowrap="nowrap" valign="BASELINE" align="RIGHT">From: </th>
<td>Shixia Liu via ieee_vis
<a class="moz-txt-link-rfc2396E" href="mailto:ieee_vis@listserv.uni-tuebingen.de"><ieee_vis@listserv.uni-tuebingen.de></a></td>
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<th nowrap="nowrap" valign="BASELINE" align="RIGHT">Reply-To:
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<td>Shixia Liu <a class="moz-txt-link-rfc2396E" href="mailto:liushixia@gmail.com"><liushixia@gmail.com></a></td>
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<p class="MsoNormal" style="margin:0in 0in
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lang="DE">-------------------------------------------------------------------------------<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">IEEE CG&A Special Issue on Visual Computing
with Deep Learning – Call for Papers<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">-------------------------------------------------------------------------------<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE"><span> </span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">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.<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE"><span> </span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">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.<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE"><span> </span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">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:<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">- Visual analytics applications<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">- Object/scene reconstruction from RGB/RGBD images<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">- Shape analysis and synthesis<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">- Appearance capture and modeling<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">- Global illumination and real-time rendering<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">- Sound synthesis and rendering<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">- Physics-based simulation of fluids and
deformable objects<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">- Performance-based face/body animation<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">- Computational photography<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">- Deep learning models and training schemes for
visual content creation<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE"><span> </span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">---------------<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">Important Date:<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">---------------<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE"><span> </span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">Final submissions due: 1 July 2018<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">Publication date: March/April 2019<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE"><span> </span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">--------------<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">Guest Editors<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">--------------<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">Kun Zhou,<span class="gmail-Apple-converted-space"> </span><a
href="mailto:kunzhou@zju.edu.cn"
style="text-decoration:underline;color:rgb(149,79,114)"
moz-do-not-send="true">kunzhou@zju.edu.cn</a><span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">Xin Tong, Microsoft Research Asia,<span
class="gmail-Apple-converted-space"> </span><a
href="mailto:xtong@microsoft.com"
style="text-decoration:underline;color:rgb(149,79,114)"
moz-do-not-send="true">xtong@microsoft.com</a><span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE"><span> </span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">--------------<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">Submission Guidelines<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">--------------<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE"><span> </span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">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<span
class="gmail-Apple-converted-space"> </span><a
href="http://www.computer.org/web/peer-review/magazines"
style="text-decoration:underline;color:rgb(149,79,114)"
moz-do-not-send="true">www.computer.org/web/peer-review/magazines</a>.
We also strongly encourage you to submit multimedia (videos,
podcasts, and so on) to enhance your article. Visit the
CG&A supplemental guidelines at<span
class="gmail-Apple-converted-space"> </span><a
href="http://www.computer.org/web/peer-review/magazines"
style="text-decoration:underline;color:rgb(149,79,114)"
moz-do-not-send="true">www.computer.org/web/peer-review/magazines</a>.<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE"><span> </span></span></p>
<p class="MsoNormal" style="margin:0in 0in
0.0001pt;font-size:11pt;font-family:Calibri,sans-serif;color:rgb(0,0,0);font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255)"><span
lang="DE">Please submit your paper using the online
manuscript submission service at<span
class="gmail-Apple-converted-space"> </span><a
href="https://mc.manuscriptcentral.com/cs-ieee"
style="text-decoration:underline;color:rgb(149,79,114)"
moz-do-not-send="true">https://mc.manuscriptcentral.com/cs-ieee</a>.
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<span
class="gmail-Apple-converted-space"> </span><a
href="mailto:cga-ma@computer.org"
style="text-decoration:underline;color:rgb(149,79,114)"
moz-do-not-send="true">cga-ma@computer.org</a>.<span></span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE"><span> </span></span></p>
<p class="MsoNormal" style="margin:0in 0in
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lang="DE">--------------<span></span></span></p>
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lang="DE">CFP Web Page:<span></span></span></p>
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lang="DE">--------------<span></span></span></p>
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lang="DE"><a
href="https://publications.computer.org/cga/2017/12/09/special-issue-visual-computing-deep-learning-call-papers/"
style="text-decoration:underline;color:rgb(149,79,114)"
moz-do-not-send="true">https://publications.computer.org/cga/2017/12/09/special-issue-visual-computing-deep-learning-call-papers/</a></span></p>
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<pre class="moz-signature" cols="72">--
Tobias Hollerer
Professor, Department of Computer Science
University of California, Santa Barbara, CA 93106-5110
<a class="moz-txt-link-abbreviated" href="mailto:holl@cs.ucsb.edu">holl@cs.ucsb.edu</a>, Office: (805)284-9395, Fax: (805)893-8553 </pre>
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