Workshop

Uncertainty Quantification for Computer Vision

Andrea Pilzer ⋅ Martin Trapp ⋅ Arno Solin ⋅ Gianni Franchi ⋅ Andrei Bursuc ⋅ Marcus Klasson ⋅ Angela Yao ⋅ TUAN-HUNG VU ⋅ Fatma Güney

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Abstract

Though start and end times here are correct, detailed schedules here may not be complete or up to date. Please be sure to cross reference the workshop's website to verify workshop schedule details if they are available on the workshop's website. (Added by CVPR.)

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.

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Schedule

Timezone: America/Los_Angeles

WQLCP: Weighted Adaptive Conformal Prediction for Robust Uncertainty Quantification Under Distribution Shifts

Shadi Alijani

The Surprising Utility of Group Partitioning in Improving Conformal Prediction of Visual Classifiers under Distributional Shifts

Kowshik Thopalli

Uncertainty Quantification for Gradient-based Explanations in Neural Networks

Mihir Mulye

D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual Occlusions in Object Recognition

Rupayan Mallick

Improving Optical Flow and Stereo Depth Estimation by Leveraging Uncertainty-Based Learning Difficulties

Jisoo Jeong