Awet Haileslassie Gebrehiwot — accepted AI-conference papers

2023

T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds

Awet Haileslassie Gebrehiwot, David Hurych, Karel Zimmermann, Patrick Pérez, Tomáš Svoboda

IROS 2023poster

Deep perception models have to reliably cope with an open-world setting of domain shifts induced by different geographic regions, sensor properties, mounting positions, and several other reasons. Since covering all domains with annotated data is technically intractable due to the endless possible va…

Cited by 4 Source ↗code ↗Scholar ↗

2023

Teachers in Concordance for Pseudo-Labeling of 3D Sequential Data

Awet Haileslassie Gebrehiwot, Patrik Vacek, David Hurych, Karel Zimmermann, Patrick Pérez, Tomás Svoboda

RA-L 2023

Automatic pseudo-labeling is a powerful tool to tap into large amounts of sequential unlabeled data. It is especially appealing in safety-critical applications of autonomous driving, where performance requirements are extreme, datasets are large, and manual labeling is very challenging. We propose t

Cited by 7 Source ↗code ↗Scholar ↗