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Michael Schmidt

3 accepted papers

2023

Segmenting Known Objects and Unseen Unknowns without Prior Knowledge

ICCV 2023poster

Panoptic segmentation methods assign a known class to each pixel given in input. Even for state-of-the-art approaches, this inevitably enforces decisions that systematically lead to wrong predictions for objects outside the training categories. However, robustness against out-of-distribution samples…

Cited by 10PDFcodeScholar
2022

3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection

CVPR 2022poster

As 3D object detection on point clouds relies on the geometrical relationships between the points, non-standard object shapes can hinder a method's detection capability. However, in safety-critical settings, robustness to out-of-domain and long-tail samples is fundamental to circumvent dangerous iss…

Cited by 69PDFcodeScholar
2019

Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data

NeurIPS 2019poster

Continuous-time Bayesian Networks (CTBNs) represent a compact yet powerful framework for understanding multivariate time-series data. Given complete data, parameters and structure can be estimated efficiently in closed-form. However, if data is incomplete, the latent states of the CTBN have to be es…

Cited by 10SourcePDFScholar