Uncertainty Quantification for Computer Vision Call for Papers

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Call for Papers

Uncertainty Quantification for Computer Vision
Workshop & Challenge at CVPR 2025 (4th Edition)

https://uncertainty-cv.github.io/2025/

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Submission Deadline Mar 14th AOE

Two types of paper are welcome:

- Regular Papers -

(novel contributions not published previously)

- Extended Abstracts -

(novel contributions or papers that have been already accepted for publication previously)

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Apologies for multiple posting

Please distribute this call to interested parties

 

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 CVPR 2025 workshop on Uncertainty Quantification for Computer
Vision will consider recent advances in methodology and applications
of uncertainty quantification in computer vision. Prospective authors
are invited to submit papers on relevant algorithms and applications
including, but not limited to:

    Applications of uncertainty quantification in computer vision

    Reliability of Multi-modal Models (e.g., Vision-Language)

    Uncertainty Quantification of Open-Vocabulary approaches

    Failure prediction (e.g., Out-of-Distribution detection)

    Robustness in computer vision

    Safety critical applications (e.g., autonomous driving, medical diagnosis)

    Domain-shift in computer vision

    Deep probabilistic models

    Methods for uncertainty quantification

    Incorporating explicit prior knowledge in deep learning

    Output ambiguity, label noise, and diversity

    Uncertainty Quantification of GenAI approaches

All papers will be peer-reviewed, and accepted Regular papers are presented at the workshop and included in the CVPR Workshop Proceedings.

 

Challenge

The UNCV workshop will run the BRAVO Challenge 2025, focusing on
stress-testing the reliability of semantic segmentation models under
realistic perturbations and unknown out-of-distribution (OOD)
scenarios. The BRAVO dataset is organized into six subsets, two with
real data and four based on the validation set of Cityscapes with
synthetic augmentations. It spans a range of corner-cases as follows:
adverse weather conditions, OOD objects, visibility impediments (rain
drops, flares), random backgrounds to assess spurious correlations.

More information about the MUAD dataset and its download link are
available at MUAD website.

Submission Instructions

At the time of submission authors must indicate the desired paper track:

    Regular papers will be peer-reviewed following the same policy of
    the main conference and will be published in the proceedings (call
    for papers with guidelines and template here, max 8 pages,
    additional pages for references only are allowed). These are meant
    to present novel contributions not published previously (submitted
    papers should not have been published, accepted or under review
    elsewhere).

    Extended abstracts are meant for preliminary works and short
    versions of papers that have already been accepted, or are under
    review, preferably in the last year in some major conferences or
    journals. These papers will undergo a separate reviewing process
    to assess the suitability for the workshop. These will *not
    appear* in the workshop proceedings. Template and guidelines (max
    4 content pages, additional pages for references allowed) here.

 

Submission site: 
https://openreview.net/group?id=thecvf.com/CVPR/2025/Workshop/UnCV

 

Important Dates (All times are end of day AOE)

Submission deadline: Mar 14th, 2025

Notification of acceptance: April 1st, 2025

Camera-ready deadline: April 7th, 2025

 

Organizing Commitee

    Andrea Pilzer, NVIDIA, Italy

    Gianni Franchi, ENSTA Paris, France

    Andrei Bursuc, valeo.ai, France

    Arno Solin, Aalto University, Finland

    Martin Trapp, Aalto University, Finland

    Marcus Klasson, FCAI & Aalto University, Finland

    Angela Yao, National University of Singapore, Singapore

    Tuan-Hung Vu, valeo.ai and Inria, France

    Fatma Güney, Koç University, Turkey