[visionlist] MBCC 2017: ICCV Workshop on Mutual Benefits of Cognitive and Computer Vision - Call For Papers

Ali Borji aliborji at gmail.com
Thu Jun 15 13:14:04 -04 2017


ICCV International Workshop on

‘Mutual Benefits of Cognitive and Computer Vision (MBCC)’

29 October 2017

Venice, Italy



Aim and Scope

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.

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?

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.


Topics for submission include but are not limited to:


   architectures for processing visual information in the human brain and
   computer vision (e.g. feedforward vs feedback, shallow vs deep networks,
   residual, recurrent, etc)

   limitations of existing computer vision/deep learning systems compared
   to human vision

   learning rules employed in computer vision and by the brain (e.g.
   unsupervised/semi-supervised learning, Hebb rule, Spike timing dependent

   representations/features in humans and computer vision

   tasks/metrics to compare human and computer vision (e.g. eye fixation,
   reaction time, rapid categorization, visual search)

   new benchmarks (e.g. datasets)

   generalizability of machine representation to other tasks

   new techniques to measure and analyze human psychophysics and neural

   the problem on invariant learning

   conducting large-scale behavioral and physiological experiments (e.g.,
   fMRI, cell recording)


Invited Speakers

We have invited leading researchers from both Cognitive Science and
Computer Vision to inspire discussions and collaborations.


   Michael Tarr, Carnegie Mellon University



Submission Guidelines

We are inviting both full paper (5-8 pages) and extended abstract (2-4
pages) submissions to the workshop. Submitted papers must follow the ICCV
paper format and guidelines (available on the ICCV 2017 webpage). All
submissions will be handled via the CMT website:  https://cmt3.research.

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.

Extended abstracts: We invite submissions of extended abstracts of ongoing
or already published work as well as demos or prototype systems (ICCV
format). Authors are given the opportunity to present their work to right
audience. The review will be single-blind.


Important Dates

Full Paper submission:  August 1st, 2017

Extended Abstract submission: August 5th, 2017

Notification of acceptance:  August 15th, 2017

Camera-ready paper due: September 30th, 2017

Workshop:  October 29th, 2017 (Morning Session)


Workshop Organizing Committee

Ali Borji, University of Central Florida

Pramod RT, Indian Institute of Science

Elissa Aminoff, Fordham University

Christopher Kanan, Rochester Institute of Technology


mbccw.iccv2017 at gmail.com
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