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Chapter and Conference Paper
Quantification of Predictive Uncertainty via Inference-Time Sampling
Predictive variability due to data ambiguities has typically been addressed via construction dedicated models with built-in probabilistic capabilities that are trained to predict uncertainty estimates as vari...
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Chapter and Conference Paper
Mind the Gap: Modeling Local and Global Context in (Road) Networks
We propose a method to label roads in aerial images and extract a topologically correct road network. Three factors make road extraction difficult: (i) high intra-class variability due to clutter like cars, ma...
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Article
Inference Methods for CRFs with Co-occurrence Statistics
The Markov and Conditional random fields (CRFs) used in computer vision typically model only local interactions between variables, as this is generally thought to be the only case that is computationally tract...
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Chapter and Conference Paper
What, Where and How Many? Combining Object Detectors and CRFs
Computer vision algorithms for individual tasks such as object recognition, detection and segmentation have shown impressive results in the recent past. The next challenge is to integrate all these algorithms ...
