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A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation
M. M. A. Valiuddin, R. J. G. van Sloun, C. G. A. Viviers +2
Advances in architectural design, data availability, and compute have driven remarkable progress in semantic segmentation. Yet, these models often rely on relaxed Bayesian assumpti…
Can Your Generative Model Detect Out-of-Distribution Covariate Shift?
Christiaan Viviers, Amaan Valiuddin, Francisco Caetano +4
Detecting Out-of-Distribution (OOD) sensory data and covariate distribution shift aims to identify new test examples with different high-level image statistics to the captured, nor…
Find the Assembly Mistakes: Error Segmentation for Industrial Applications
Dan Lehman, Tim J. Schoonbeek, Shao-Hsuan Hung +3
Recognizing errors in assembly and maintenance procedures is valuable for industrial applications, since it can increase worker efficiency and prevent unplanned down-time. Although…