Uncertainty and Robustness in Deep Visual Learning

CVPR 2019 Workshop

June 16 or 17, 2019, Long Beach, California.


The past decade was marked by significant progress in the field of artificial intelligence and statistical learning. Efficient new algorithms, coupled with the availability of large datasets and dramatic increase in computing power, led to solutions that match, or exceed, human performance in perception tasks such as image and speech recognition, as well as in building strategies for complex board and computer games. Deep learning technologies are being widely applied across different areas, from new art form creation to subatomic particle and drug discovery. However, the most efficient of modern models come in form of a black box, with the majority of them lacking the ability to robustly reason about the confidence of their predictions. At the same time, being capable to quantify model uncertainty and recognize failure scenarios is crucial when it comes to incorporating them into complex decision making pipelines, e.g. autonomous driving or medical image analysis systems.

This workshop will bring together researchers focused on the discovery of new architectures and statistical methods for robust predictive models, as well as computer vision and machine learning practitioners from the industry that work on applying those models in complex real-world scenarios. The goal of the workshop is to build a solid understanding of how classical uncertainty quantification methods could be applied in the deep learning era. It will also provide the discussion of methods developed over the past decade and solutions for efficient industrial applications.

Covered topics

  • Uncertainty quantification;
  • Deep probabilistic models;
  • Failure prediction;
  • Bayesian methods;
  • Robust vision in autonomous driving, robotics, medical imaging;
  • Open-world, lifelong learning;
  • Uncertainty quantification in domain transfer, active learning, adversarial attack defense.

Invited speakers

Alex Kendall

Wayve, University of Cambridge

Raquel Urtasun

Uber, University of Toronto

Sergey Levine

UC Berkeley

Kris Kitani

Carnegie Mellon University

Andreas Geiger

Max Planck Institute for Intelligent Systems, University of Tübingen

Call for papers

We invite submission of 2-3 page extended abstracts ( using the CVPR 2019 format) describing work in the domains suggested above or in closely-related areas. Accepted papers will be presented during the spotlight\poster session at the workshop. Authors may submit a draft of their poster as an optional fourth page. The extended abstract and poster sketch should be submitted as a single PDF file with no more than 3-4 pages (excluding references).

Submissions of work which have been previously published, including papers accepted to the main CVPR 2019 conference, are allowed.

Paper submission deadline: April 15, 2019

Author notification: May 15, 2019


Sergey Prokudin

Max Planck Institute for Intelligent Systems

Kevin Murphy

Google AI

Zeynep Akata

Univesity of Amsterdam