[visionlist] Domain Adaptation for Visual Understanding at IJCAI/ECAI/AAMAS/ICML 2018

[Mayank Vatsa]

Workshop on Domain Adaptation for Visual Understanding (DAVU)
Joint IJCAI/ECAI/AAMAS/ICML 2018 Workshop

https://cmt3.research.microsoft.com/DAVU2018/

http://iab-rubric.org/ijcai-davu.html

Paper Submission Deadline: May 10, 2018
Note: Extended version of accepted papers will be invited for consideration
in one of the prestigious journals (approval pending).

Visual understanding is a fundamental cognitive ability in humans which is
essential for identifying objects/people and interacting in social space.
This cognitive skill makes interaction with the environment extremely
effortless and provides an evolutionary advantage to humans as a species.
In our daily routines, we, humans, not only learn and apply knowledge for
visual recognition, we also have intrinsic abilities of transferring
knowledge between related visual tasks, i.e., if the new visual task is
closely related to the previous learning, we can quickly transfer this
knowledge to perform the new visual task. In developing machine learning
based automatedvisual recognition algorithms, it is desired to utilize
these capabilities to make the algorithms adaptable. Generally traditional
algorithms, given some prior knowledge in a related visual recognition
task, do not adapt to a new task and have to learn the new task from the
beginning. These algorithms do not consider that the two visual tasks may
be related and the knowledge gained in one may be used to learn the new
task efficiently in lesser time. Domain adaptation for visual understanding
is the area of research, which attempts to mimic this human behavior by
transferring the knowledge learned in one or more source domains and use it
for learning the related visual processing task in target domain. Recent
advances in domain adaptation, particularly in co-training, transfer
learning, and online learning have benefited the computer vision
significantly. For example, learning from high-resolution source domain
images and transferring the knowledge to learning low-resolution target
domain information has helped in building improved cross-resolution face
recognition algorithms. This special issue will focus on the recent
advances on domain adaptation for visual recognition. The organizers invite
researchers to participate and submit their research papers in the Domain
Adaptation workshop. Topics of interest include but are not limited to:

A. Novel algorithms for visual recognition using
1. Co-training
2. Transfer learning
3. Online (incremental/decremental) learning
4. Covariate shift
5. Heterogeneous domain adaptation
6. Dataset bias

B. Domain adaptation in visual representation learning using
1. Deep learning
2. Shared representation learning
3. Online (incremental/decremental) learning
4. Multimodal learning
5. Evolutionary computation-based domain adaptation algorithms

C. Applications in computer vision such as
1. Object recognition
2. Biometrics
3. Hyper-spectral
4. Surveillance
5. Road transportation
6. Autonomous driving

*Submission Format: *The authors should follow IJCAI paper preparation
instructions,
including page length (e.g. 6 pages + 1 extra page for reference).

*Important Dates: *
Submission deadline: May 10, 2018
Decision notification: May25, 2018

*Paper Submission Page: https://cmt3.research.microsoft.com/DAVU2018/
<https://cmt3.research.microsoft.com/DAVU2018/*>

Best regards,

--
Mayank Vatsa, PhD
Vice President (Publications), IEEE Biometrics Council
Head, Infosys Center for Artificial Intelligence
Associate Professor, IIIT-Delhi, India
Adjunct Associate Professor, West Virginia University, USA
http://iab-rubric.org/
http://cai.iiitd.ac.in/
http://ieee-biometrics.org/
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