[visionlist] [CFP] ICCV 2017 Workshop on Transferring and Adapting Source Knowledge in Computer Vision + Challenge!

Tatiana Tommasi tommasi.t at gmail.com
Wed May 3 19:38:39 -05 2017

##               1st Call for Papers                ##
ICCV workshop on Transferring and Adapting Source Knowledge in Computer
Vision (TASK-CV) 2017
Venice, Italy, 29 October, 2017

Workshop site: http://adas.cvc.uab.es/task-cv2017/
Challenge site: http://ai.bu.edu/visda-2017/

Important Dates
Challenge devkit/data release: see the challenge website!

Submission deadline: June 21st, 2017
Author notification: August 4th, 2017
Camera-ready due: August 14th, 2017
Workshop date:  29 October, 2017


This would be the 4th annual workshop that brings together computer vision
researchers interested in domain adaptation and knowledge transfer
techniques. New this year would be the proposed Domain Adaptation
Challenge, see below.

A key ingredient of the recent successes in computer vision has been the
availability of visual data with annotations, both for training and
testing, and well-established protocols for evaluating the results.
However, this traditional supervised learning framework is limited when it
comes to deployment on new tasks and/or operating in new domains. In order
to scale to such situations, we must find mechanisms to reuse the available
annotations or the models learned from them.


Accordingly, TASK-CV aims to bring together research in transfer learning
and domain adaptation for computer vision and invites the submission of
research contributions on the following topics:

-TL/DA learning methods for challenging paradigms like unsupervised, and
incremental or on-line learning.
-TL/DA focusing on specific visual features, models or learning algorithms.
-TL/DA jointly applied with other learning paradigms such as reinforcement
-TL/DA in the era of deep neural networks (e.g., CNNs), adaptation effects
of fine-tuning, regularization techniques, transfer of architectures and
weights, etc.
-TL/DA focusing on specific computer vision tasks (e.g., image
classification, object detection, semantic segmentation, recognition,
retrieval, tracking, etc.) and applications (biomedical, robotics,
multimedia, autonomous driving, etc.).
-Comparative studies of different TL/DA methods.
-Working frameworks with appropriate CV-oriented datasets and evaluation
protocols to assess TL/DA methods.
-Transferring knowledge across modalities (e.g., learning from 3D data for
recognizing 2D data, and heterogeneous transfer learning)
-Transferring part representations between categories.
-Transferring tasks to new domains.
-Solving domain shift due to sensor differences (e.g., low-vs-high
resolution, power spectrum sensitivity) and compression schemes.
-Datasets and protocols for evaluating TL/DA methods.

This is not a closed list; thus, we welcome other interesting and relevant
research for TASK-CV.

Domain Adaptation Challenge

This year we are pleased to announce an accompanying Visual Domain
Adaptation challenge. Please see  the challenge website (
http://ai.bu.edu/visda-2017/ ) for details, dates, and submission

Best Paper Award

As in previous workshops, we plan to award 1-2 best papers. Sponsors will
be announced.


- All submissions will be handled via the CMT website https://cmt3.research.
- The format of the papers is the same as the ICCV main conference. The
contributions will consist in Extended Abstracts (EA) of 6 pages (excluding
the references).
- We accept dual submissions to ICCV 2016 and TASK-CV 2017. In other words,
the submission to TASK-CV 2017 should be a 6-page summary of the submission
to ICCV 2017 (Authors need to indicate the difference in their ICCV
camera-ready version after their paper accepted by ICCV).
- Submissions will be rejected without review if they: exceed the page
limitation or violate the double-blind policy.
- Manuscript templates can be found at the main conference website:
- The accepted papers will be included in the ICCV workshop collections,
and also linked in the TASK-CV webpage.

Workshop Chairs

Tatiana Tommasi, University of Rome La Sapienza, Italy
Kate Saenko, Boston University, USA
Ben Usman, Boston University, USA
Xingchao Peng, Boston University, USA
Judy Hoffman, Stanford, USA
Dequan Wang, UC Berkeley, USA
Antonio M. López, Computer Vision Center & U. Autònoma de Barcelona, Spain
Wen Li – ETH Zurich, Switzerland
Francesco Orabona, Stony Brook University, USA
David Vázquez, Computer Vision Center & U. Autònoma de Barcelona, Spain
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