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

Tatiana Tommasi tommasi.t at gmail.com
Sun May 17 19:29:28 -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.


• 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.

Submission deadline: July 10th, 2020
Author notification: July 26th, 2020
Camera-ready: August 15th, 2020

The contributions will consist in Extended Abstracts (EA) of 4 pages
(including references)

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


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.

• May 1: training/validation data release; evaluation server open
• Jun 25: test data release
• Jul 25: final test result submission
• Team registration is open until July 25.

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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