6th workshop on Transferring and Adapting Source Knowledge in Computer Vision and 3rd VisDA ChallengCe all for Papers

6th workshop on Transferring and Adapting Source Knowledge in Computer Vision and

3rd VisDA Challenge

In conjunction with International Conference on Computer Vision (ICCV) 2019
2 Novembre 2019, Seoul, Korea


This is the 6th annual workshop that brings together computer vision
researchers interested in domain adaptation and knowledge transfer

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.

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

- TL/DA/DG learning methods for challenging paradigms like
unsupervised, self-supervised, incremental, and online learning;

- TL/DA/DG focusing on specific visual features, models or learning

- TL/DA/DG applied for procedural learning (e.g. state-action pairs,
reinforcement learning);

- TL/DA/DG in the era of convolutional neural networks (CNNs),
adaptation effects of fine-tuning, regularization techniques, transfer
of architectures and weights, etc;

- TL/DA/DG 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/DG methods;

- Working frameworks with appropriate datasets and evaluation
protocols to assess TL/DA/DG methods;

- 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;

- Extensions of few- and zero-shot learning approaches across domains.

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

Paper Submission: July 10th, 2019
Notification of Acceptance: July 22nd, 2019
Camera-Ready: August 10th, 2019

As tradition we will have a best paper award supported by our sponsors

Tatiana Tommasi, Politecnico di Torino, Italy
David Vázquez, Element AI, Canada
Kate Saenko, Boston University, USA
Ben Usman, Boston University, USA
Xingchao Peng, Boston University, USA
Judy Hoffman, Facebook AI Research, USA
Neela Kaushik, Boston University, USA
Antonio M. López, Universitat Autňnoma de Barcelona (UAB) and Computer Vision Center (CVC), Spain
Wen Li, ETH Zurich, Switzerland