*EXTENDED DEADLINE* ECCV Workshop on Transferring and Adapting Source Knowledge & VISDA Challenge
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Tatiana Tommasi
Jul 2, 2018, 2:00:19 PM7/2/18
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======================================================
3rd Call for Papers -- EXTENDED DEADLINE
======================================================
ECCV TASK-CV Workshop on Transferring and Adapting Source Knowledge
in Computer Vision & VisDA Challenge
Munich, September 14th 2018
Workshop site: https://sites.google.com/view/task-cv2018/home
Challenge site: http://ai.bu.edu/visda-2018/
Important Dates
Paper Track
Submission: *July 9th, 2018* EXTENDED!
Notification: July 15th, 2018
Camera Readay: July 25th, 2018
*New: Best Paper Award*
Among the accepted papers, the program committee will select and
award the best paper with a prize of 600€ offered by our sponsors.
Challenge
Registration: April 21st , 2018
Train and Validation data release: May 16th, 2018
Test data release: August 1st, 2018
Notification win.: September 1st, 2018
Workshop Topics
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 and generalize to new
domains and tasks.
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 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.)
■ TL/DA focusing on specific visual features, models or learning algorithms for
challenging paradigms like unsupervised, reinforcement, or online learning
■ TL/DA in the era of convolutional neural networks (CNNs), adaptation effects
of fine-tuning, regularization techniques, transfer of architectures and weights, etc.
■ Comparative studies of different TL/DA methods and transferring part representations
between categories and 2D/3D modalities
■ Working frameworks with appropriate CV-oriented datasets and evaluation
protocols to assess TL/DA
This is not a closed list; thus, we welcome other related research for TASK-CV.
VisDA Challenge
The VisDA challenge aims to test domain adaptation methods’ ability to transfer
source knowledge and adapt it to novel target domains.
Organizers
Tatiana Tommasi , IIT Milan-Italy
David Vázquez, Element AI
Kate Saenko, Boston University
Ben Usman, Boston University
Xingchao Peng, Boston University
Judy Hoffman, UC Berkeley
Neela Kaushik, Boston University
Kuniaki Saito, Boston University
Antonio M. López, UAB/CVC
Wen Li, ETH Zurich
Francesco Orabona, Boston University
3rd Call for Papers -- EXTENDED DEADLINE
======================================================
ECCV TASK-CV Workshop on Transferring and Adapting Source Knowledge
in Computer Vision & VisDA Challenge
Munich, September 14th 2018
Workshop site: https://sites.google.com/view/task-cv2018/home
Challenge site: http://ai.bu.edu/visda-2018/
Important Dates
Paper Track
Submission: *July 9th, 2018* EXTENDED!
Notification: July 15th, 2018
Camera Readay: July 25th, 2018
*New: Best Paper Award*
Among the accepted papers, the program committee will select and
award the best paper with a prize of 600€ offered by our sponsors.
Challenge
Registration: April 21st , 2018
Train and Validation data release: May 16th, 2018
Test data release: August 1st, 2018
Notification win.: September 1st, 2018
Workshop Topics
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 and generalize to new
domains and tasks.
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 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.)
■ TL/DA focusing on specific visual features, models or learning algorithms for
challenging paradigms like unsupervised, reinforcement, or online learning
■ TL/DA in the era of convolutional neural networks (CNNs), adaptation effects
of fine-tuning, regularization techniques, transfer of architectures and weights, etc.
■ Comparative studies of different TL/DA methods and transferring part representations
between categories and 2D/3D modalities
■ Working frameworks with appropriate CV-oriented datasets and evaluation
protocols to assess TL/DA
This is not a closed list; thus, we welcome other related research for TASK-CV.
VisDA Challenge
The VisDA challenge aims to test domain adaptation methods’ ability to transfer
source knowledge and adapt it to novel target domains.
Organizers
Tatiana Tommasi , IIT Milan-Italy
David Vázquez, Element AI
Kate Saenko, Boston University
Ben Usman, Boston University
Xingchao Peng, Boston University
Judy Hoffman, UC Berkeley
Neela Kaushik, Boston University
Kuniaki Saito, Boston University
Antonio M. López, UAB/CVC
Wen Li, ETH Zurich
Francesco Orabona, Boston University
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