posted by organizer: hrtavakoli || 1935 views || tracked by 3 users: [display]

MBCC 2018 : CVPR 2nd International Workshop on ‘Mutual Benefits of Cognitive and Computer Vision (MBCC)’

FacebookTwitterLinkedInGoogle

Link: https://sites.google.com/site/mbcc2018w/home
 
When Jun 22, 2018 - Jun 22, 2018
Where Salt Lake City
Submission Deadline Mar 25, 2018
Notification Due Apr 1, 2018
Final Version Due Apr 8, 2018
Categories    computer vision   artificial intelligence   computer science
 

Call For Papers

_____________
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:
-------------------------------------------------------------------------
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)
o limitations of existing computer vision/deep learning systems compared to human vision
o learning rules employed in computer vision and by the brain (e.g. unsupervised/semi-supervised learning, Hebb rule, Spike timing dependent plasticity)
o representations/features in humans and computer vision
o tasks/metrics to compare human and computer vision (e.g. eye fixation, reaction time, rapid categorization, visual search)
o new benchmarks (e.g. datasets)
o generalizability of machine representation to other tasks
o new techniques to measure and analyze human psychophysics and neural signals
o the problem on invariant learning
o 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.

1. TBA
2. TBA

___________________
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 CVPR paper format and guidelines (available on the CVPR 2018 webpage). All submissions will be handled via the CMT website: https://cmt3.research.microsoft.com/MBCC2018/

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 (CVPR format). Authors are given the opportunity to present their work to the right audience. The review will be single-blind.

______________
Important Dates
------------------------
Full Paper submission: March 25th, 2018
Extended Abstract submission: March 20th, 2018
Notification of acceptance: April 1st, 2018
Camera-ready paper due: April 8th, 2018
Workshop: June 22nd, 2018

_____________________________
Workshop Organizing Committee
---------------------------------------------------
Ali Borji, University of Central Florida
Krista A. Ehinger, York University
Odelia Schwartz, University of Miami
Gregory Zelinsky, Strony Brook University
Hamed R. Tavakoli, Aalto University

Related Resources

ICCV 2019   International Conference on Computer Vision
IJCAI 2019   International Joint Conference on Artificial Intelligence
CVPR 2019   Computer Vision and Pattern Recognition
CVPR 2018   Computer Vision and Pattern Recognition
IROS 2019   Intelligent RObots and Systems
CAIP 2019   Computer Analysis of Images and Patterns
ICDMML 2019   【ACM ICPS EI SCOPUS】2019 International Conference on Data Mining and Machine Learning
ICML 2019   36th International Conference on Machine Learning
SSCI 2019   The 2019 IEEE Symposium Series on Computational Intelligence
ISAIR 2019   The 4th International Symposium on Artificial Intelligence and Robotics 2019