In the last decade, substantial progress has been made w.r.t. the performance of computer vision systems, a significant part of it thanks to deep learning. These advancements prompted sharp community growth and a rise in industrial investment. However, most current models lack the ability to reason about the confidence of their predictions; integrating uncertainty quantification into vision systems will help recognize failure scenarios and enable robust applications.
The UNcertainty quantification for Computer Vision (UNCV) Workshop aims to raise awareness and generate discussion regarding how predictive uncertainty can, and should, be effectively incorporated into models within the vision community. At the time of Generative AI (GenAI) we find this more crucial than ever. The workshop will bring together experts from machine learning and computer vision to create a new generation of well-calibrated and effective methods that know when they do not know.
We invite submissions on topics, such as, but not limited to:
We invite two types of submissions: regular papers (that will follow CVPR format, published in proceedings) and extended abstracts (short max 4 pages papers, not published in proceedings).
All submissions will be peer-reviewed, and accepted submissions will be presented at the workshop. Only regular CVPR workshop papers will be included in the Workshop Proceedings. All submission should follow the CVPR author guidelines using the provided author kit.
All times are end of day AOE.
