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<font style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;" size="1"><span style="font-size:12.79px;">CVPR International Workshop on</span></font><br>
<font style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;" size="1"><span style="font-size:12.79px;">‘Mutual Benefits of Cognitive and Computer Vision (</span></font><font style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;" size="1"><span style="font-size:12.79px;">MBCC</span></font><font style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;" size="1"><span style="font-size:12.79px;">)’</span></font><br>
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<font style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;" size="1"><span style="font-size:12.79px;">_____________</span></font><br>
<font style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;" size="1"><span style="font-size:12.79px;">Aim and Scope</span></font>
<div style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;"><font size="1"><span style="font-size:12.79px;">-----------------------<br>
As researchers working at the intersection of biological and machine
vision, we have noticed an increasing interest in both communities to
understand and improve on each other’s insights. Recent advances in
machine learning (especially deep learning) have led
to unprecedented improvements in computer vision. These deep learning
algorithms have revolutionized computer vision, and now rival humans at
some narrowly defined tasks such as object recognition (e.g., the
ImageNet Large Scale Visual Recognition Challenge).
In spite of these advances, the existence of adversarial images (some
of which have perturbations imperceptible to humans) and rather poor
generalizability across datasets point out the flaws present in these
networks. On the other hand, the human visual system
remains highly efficient at solving real-world tasks and capable of
solving many visual tasks. We believe that the time is ripe to have
extended discussions and interactions between researchers from both
fields in order to steer future research in more fruitful
directions. This workshop will compare human vision to state-of-the-art
machine perception methods, with specific emphasis on deep learning
models and architectures.<br>
<br>
Our workshop will address many important questions. They include: 1)
What are the representational differences between human and machine
perception? 2) What makes human vision so effective? and 3) What can we
learn from human vision research? Addressing these
questions is not as difficult as previously thought due to
technological advancements in both computational science and
neuroscience. We can now measure human behavior precisely and collect
huge amounts of neurophysiological data using EEG and fMRI. This places
us in a unique position to compare state-of-the-art computer vision
models and human behavioral/neural data, which was impossible to do a
few years ago. However, this advantage also comes with its own set of
problems: Which task/metric to use for comparison?
What are the representational similarities? How different are the
computations in a biological visual system when compared to an
artificial vision system? How does human vision achieve invariance?<br>
<br>
This workshop is a great opportunity for researchers working on human
and/or machine perception to come together and discuss plausible
solutions to some of the aforementioned problems.<br>
<br>
</span></font></div>
<div style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;"><font size="1"><span style="font-size:12.79px;">______________________________ ____________<br>
Topics for submission include but are not limited to:<br>
------------------------------ ------------------------------ -------------<br>
o architectures for processing visual information in the human brain and
computer vision (e.g. feedforward vs feedback, shallow vs deep
networks, residual, recurrent, etc)<br>
o limitations of existing computer vision/deep learning systems compared to human vision<br>
o learning rules employed in computer vision and by the brain (e.g.
unsupervised/semi-supervised learning, Hebb rule, Spike timing dependent
plasticity)<br>
o representations/features in humans and computer vision<br>
o tasks/metrics to compare human and computer vision (e.g. eye fixation, reaction time, rapid categorization, visual search)<br>
o new benchmarks (e.g. datasets)<br>
o generalizability of machine representation to other tasks<br>
o new techniques to measure and analyze human psychophysics and neural signals<br>
o the problem on invariant learning<br>
o conducting large-scale behavioral and physiological experiments (e.g., fMRI, cell recording)<br>
<br>
______________<br>
Invited Speakers</span></font></div>
<div style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;"><font size="1"><span style="font-size:12.79px;">------------------------<br>
We have invited leading researchers from both Cognitive Science and Computer Vision to inspire discussions and collaborations.<br>
<br>
</span></font></div>
<div style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;"><font size="1"><span style="font-size:12.79px;">1. TBA<br>
2. TBA<br>
<br>
___________________<br>
Submission Guidelines<br>
------------------------------ ---</span></font></div>
<div style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;"><font size="1"><span style="font-size:12.79px;">We
are inviting both full paper (5-8 pages) and extended abstract (2-4
pages) submissions to the workshop. Submitted papers must follow the
CVPR paper format and guidelines (available on the CVPR 2018 webpage).
All submissions will be handled via the CMT website: <a href="https://cmt3.research.microsoft.com/MBCC2018/" id="ydp8fa4e6f1yiv0992505048m_-8562893669363151617m_-6103388469858288929LPlnk298356" rel="nofollow" target="_blank"><span style="color:rgb(255,0,0);">https://cmt3.research .microsoft.com/MBCC2018/</span></a><span style="color:rgb(255,0,0);"> </span><br>
<br>
Full papers: The submitted papers should have a maximum length of 8
pages, including figures and tables; additional pages must contain only
cited references. The review will be double-blind. Please make sure all
authors or references to authors are anonymized.
Full paper submissions must not have been published before.<br>
<br>
Extended abstracts: We invite submissions of extended abstracts of
ongoing or already published work as well as demos or prototype systems
(CVPR format). Authors are given the opportunity to present their work
to the right audience. The review will be single-blind.<br>
<br>
______________<br>
Important Dates<br>
------------------------<br>
Full Paper submission: March 10th, 2018<br>
Extended Abstract submission: March 20th, 2018<br>
Notification of acceptance: April 1st, 2018<br>
Camera-ready paper due: April 7th, 2018<br>
Workshop: June 22nd, 2018 <br>
<br>
</span></font></div>
<div style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;"><font size="1"><span style="font-size:12.79px;">_____________________________<br>
</span></font></div>
<div style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;"><font size="1"><span style="font-size:12.79px;">Workshop Organizing Committee<br>
------------------------------ ---------------------<br>
Ali Borji, University of Central Florida<br>
Krista A. Ehinger, York University<br>
Odelia Schwartz, <span class="ydp8fa4e6f1yiv0992505048m_-8562893669363151617m_-6103388469858288929st">University of Miami</span><br>
Gregory Zelinsky, Strony Brook University<br>
Hamed R. Tavakoli, Aalto University<br>
</span></font></div>
<div><font style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12pt;" size="1"><span style="font-size:12.79px;">_______<br>
Contact</span></font><br style="font-size:12.79px;"><span style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12.79px;">---------</span><span style="font-family:Calibri, Helvetica, sans-serif;font-size:12.79px;">--</span><span style="font-family:Calibri, Helvetica, sans-serif;font-size:12.79px;">--</span></div><div><span style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12.79px;">Ali Borji, </span><font face="Calibri, Helvetica, sans-serif"><span style="font-size:12.79px;"><a href="mailto:aborji@crcv.ucf.edu" rel="nofollow" target="_blank">aborji@crcv.ucf.edu</a></span></font><br style="font-size:12.79px;"><span style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12.79px;">Krista A. Ehinger, </span><font face="Calibri, Helvetica, sans-serif"><span style="font-size:12.79px;"><a href="mailto:kehinger@yorku.ca" rel="nofollow" target="_blank">kehinger@yorku.ca</a></span></font><br style="font-size:12.79px;"><span style="color:rgb(0, 0, 0);font-family:Calibri, Helvetica, sans-serif;font-size:12.79px;">Hamed R. Tavakoli, </span><font><font face="Calibri, Helvetica, sans-serif"><span style="font-size:12.79px;"><a href="mailto:hamed.r-tavakoli@aalto.fi" rel="nofollow" target="_blank">hamed.r-tavakoli@aalto.fi</a></span></font><br>
<br>
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