Workshop on Adversarial Robustness in the Real World Call for Papers
ICCV2021 - 2nd Workshop on Adversarial Robustness in the Real World
11th October 2021
In conjunction with ICCV 2021
Web: https://iccv21-adv-workshop.github.io/
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IMPORTANT DATES
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Full Paper Submission: August 5th, 2021, Anywhere on Earth (AoE)
Notification of Acceptance: August 12th, 2021, Anywhere on Earth (AoE)
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CALL FOR PAPERS
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Computer vision systems nowadays often perform on super-human level in
complex cognitive tasks but research in adversarial machine learning
also shows that they are not as robust as the human vision system. In
this context, perturbation-based adversarial examples have received
great attention.
Recent work has shown that deep neural networks are also easily
challenged by real-world adversarial examples, e.g. including partial
occlusion, viewpoint changes, atmospheric changes, or style
changes. Discovering and harnessing those adversarial examples helps
us understand and improve the robustness of computer vision methods in
real-world environments, which will also accelerate the deployment of
computer vision systems in safety-critical applications. In this
workshop, we aim to bring together researchers from various fields,
including robust vision, adversarial machine learning, and explainable
AI, to discuss recent research and future directions for adversarial
robustness and explainability, with a particular focus on real-world
scenarios.
Topics include but are not limited to:
discovery of real-world adversarial examples
novel architectures with robustness to occlusion, viewpoint, and
other real-world domain shifts
domain adaptation techniques for building robust vision system in
the real world
datasets for evaluating model robustness
adversarial machine learning for diagnosing and understanding
limitations of computer vision systems
improving generalization performance of computer vision systems to
out-of-distribution samples
structured deep models and explainable AI
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INVITED SPEAKERS
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- Kate Saenko, Boston University
- Alan Yuille, Johns Hopkins University
- Cihang Xie, University of California, Santa Cruz
- Aleksander Madry, MIT
- Ludwig Schmidt, MIT
- Tomaso Poggio, MIT
- Nicholas Carlini, Google
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SUBMISSION AND REVISION
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Submissions need to be anonymized and follow the ICCV 2021 Author Instructions.
http://iccv2021.thecvf.com/node/4#submission-guidelines
The workshop considers two types of submissions: (1) Long Paper:
Papers are limited to 8 pages excluding references and will be
included in the official ICCV proceedings; (2) Extended Abstract:
Papers are limited to 4 pages excluding references and will NOT be
included in the official ICCV proceedings. Please use the ICCV
template for extended abstracts .
Based on the PC recommendations, the accepted long papers/extended
abstracts will be allocated either a contributed talk or a poster
presentation.
We invite submissions on any aspect of adversarial robustness in
real-world computer vision. This includes, but is not limited to:
Discovery of real-world adversarial examples
Novel architectures with robustness to occlusion, viewpoint, and other
real-world domain shifts
Domain adaptation techniques for building robust vision system in the
real world
Datasets for evaluating model robustness
Adversarial machine learning for diagnosing and understanding
limitations of computer vision systems
Improving generalization performance of computer vision systems to
out-of-distribution samples
Structured deep models and explainable AI
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WORKSHOP ORGANIZERS
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Yingwei Li, Johns Hopkins University
Adam Kortylewski, Johns Hopkins University
Cihang Xie, UCSC
Yutong Bai, Johns Hopkins University
Angtian Wang, Johns Hopkins University
Chenglin Yang, Johns Hopkins University
Xinyun Chen, UCB
Yinpeng Dong, Tsinghua University
Tianyu Pang, Tsinghua University
Jieru Mei, Johns Hopkins University
Nataniel Ruiz, Boston University
Alexander Robey, UPenn
Wieland Brendel, University of Tübingen
Matthias Bethge, University of Tübingen
George Pappas, UPenn
Philippe Burlina, Johns Hopkins University
Rama Chellappa, Johns Hopkins University
Dawn Song, UCB
Jun Zhu, Tsinghua University
Hang Su, Tsinghua University
Matthias Hein, University of Tübingen
Judy Hoffman, Georgia Tech
Alan Yuille, Johns Hopkins University