[visionlist] 2nd CFP - ECCV Workshop on Transferring and Adapting Source Knowledge in Computer Vision & VisDA Challenge

Tatiana Tommasi tommasi.t at gmail.com
Fri Jul 3 13:41:19 -04 2020

*7th Workshop on Transferring and Adapting Source Knowledge in Computer
Vision & 4th VisDA Challenge*
In conjunction with European Conference on Computer Vision (ECCV) 2020
23 August 2020, Glasgow, UK

Workshop website: https://sites.google.com/view/task-cv2020/home
VisDA challenge website: http://ai.bu.edu/visda-2020/

This is the 7th annual workshop that brings together computer vision
researchers interested in domain adaptation and knowledge transfer

A key ingredient of the recent successes of computer vision methods is the
availability of large sets of annotated data. However, collecting them is
prohibitive in many real applications and it is natural to search for an
alternative source of knowledge that needs to be transferred or adapted to
provide sufficient learning support. Our workshop aims to bring together
researchers in various sub-areas of Transfer Learning (TL) and Domain
Adaptation (DA) for computer vision.

*Check the website to see the amazing list of speakers of this year!*


• TL/DA learning methods for challenging paradigms like unsupervised,
incremental, open set, universal, online and federated learning
• TL/DA CNN architectures with new adaptation techniques, fine-tuning
strategies, regularization approaches, weights transfer solutions etc.
• TL/DA focusing on specific computer vision tasks (e.g., image
classification, object detection, semantic segmentation, retrieval,
tracking, etc.)  and applications (biomedical, robotics, multimedia,
autonomous driving, etc.)
• TL/DA  methods  working  at  feature  and  pixel  (generative)  level  as
 well  as jointly applied with other learning paradigms such as
reinforcement learning
• DA in case of sensor differences (e.g., low-vs-high resolution, power
spectrum sensitivity, different RGB/Depth modalities) and compression
• Datasets and protocols for evaluating TL/DA methods
• Going beyond TL/DA towards Domain Generalization (DG)
• Multi-Task, Zero- One- and Few-Shot Learning

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

***EXTENDED** Submission deadline: July 23rd, 2020*
Supplementary material deadline: July 30th, 2020
Author notification: August 13th, 2020
Video submission deadline: August 15th, 2020
Camera-ready: September 13th, 2020

- short papers: these contributions will consist in Extended Abstracts of 4
pages (including references). They may share contents with papers accepted
at ECCV or under review for any other conference.
- long papers: these contributions will consist of papers of the same
format of ECCV submissions: maximum 14 pages (excluding references). They
should be original works, not sharing content with papers under review.

The authors are highly encouraged to submit in their supplementary material
a 5 min pre-recorded oral presentation. The spotlight will be included in
the workshop upon acceptance.
Note that all the accepted papers will need a video, so a second call for
spotlights will come after author notification, but with a short deadline
(August 15th).

As tradition we will have a best paper award supported by our sponsor Naver
Labs Europe.


This year the VisDA Challenge brings on board a new task, domain adaptive
pedestrian re-identification. More challenging and practical settings are
set, characterized by a synthetic-to-real domain adaptation procedure.

• Registration started: April 1st, 2020
• Training data released: May 1st, 2020
• Testing data released: June 25st, 2020
• Final submission: July 25th, 2020, 11:59am EDT / 3:59pm UTC
• Winners notification: August, 2020

Tatiana Tommasi (Politecnico di Torino, Italy)
Antonio M. Lopez (CVC & UAB, Spain)
David Vazquez (Element AI, Canada)
Gabriela Csurka (Naver Labs Europe, France)
Kate Saenko (Boston University, USA)
Liang Zheng (Australian National University, Australia)
Xingchao Peng (Boston University, USA)
Weijian Deng (Australian National University, Australia)
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