https://uncertainty-cv.github.io/2026/

Submission Deadline: June 30th, 2026, 23:59 CEST

Two types of paper are welcome:

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 ECCV 2026 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, calibration, and uncertainty in vision foundation models
– Uncertainty estimation for large multimodal models, including vision-language and video-language models
– Hallucination detection and mitigation in multimodal and generative AI systems
– Uncertainty in diffusion, flow-matching, image/video generation, and text-to-image alignment
– Open-vocabulary recognition, detection, and segmentation under uncertainty
– Failure prediction, out-of-distribution detection, and distribution shift
– Robustness, safety, and uncertainty-aware decision making in computer vision
– Selective prediction, abstention, and human-in-the-loop systems
– Conformal prediction, risk control, and statistically grounded uncertainty guarantees
– Uncertainty in 3D vision, neural rendering, Gaussian splatting, and world models
– Uncertainty-aware synthetic data generation, data curation, and evaluation
– Deep probabilistic models and Bayesian methods for vision
– Incorporating explicit prior knowledge in deep learning and foundation models
– Output ambiguity, label noise, annotator disagreement, and diversity
– Safety-critical applications, including autonomous driving, robotics, and medical diagnosis

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

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/ECCV/2026/Workshop/UnCV

Important Dates

– Submission deadline: June 30th, 2026, 23:59 CEST
– Notification of acceptance: August 10th, 2026
– Camera-ready deadline: August 15th, 2026

Organizing Committee

– Andrea Pilzer, NVIDIA, Italy
– Gianni Franchi, ENSTA Paris, France
– Andrei Bursuc, valeo.ai, France
– Arno Solin, Aalto University, Finland
– Martin Trapp, KTH Royal Institute of Techology, Sweden
– Ziyun Li, KTH Royal Institute of Techology, Sweden
– Angela Yao, National University of Singapore, Singapore
– Tuan-Hung Vu, valeo.ai and Inria, France
– Fatma Güney, Koç University, Turkey