66 citations · 78 across the 7 of their papers we have counts for
4 papers · 1 filter
3D Adversarial Augmentations for Robust Out-of-Domain Predictions
Alexander Lehner, Stefano Gasperini, Alvaro Marcos-Ramiro +4
Since real-world training datasets cannot properly sample the long tail of the underlying data distribution, corner cases and rare out-of-domain samples can severely hinder the per…
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning
Aral Hekimoglu, Michael Schmidt, Alvaro Marcos-Ramiro
We propose a novel semi-supervised active learning (SSAL) framework for monocular 3D object detection with LiDAR guidance (MonoLiG), which leverages all modalities of collected dat…
Active Learning for Object Detection with Non-Redundant Informative Sampling
Aral Hekimoglu, Adrian Brucker, Alper Kagan Kayali +2
Curating an informative and representative dataset is essential for enhancing the performance of 2D object detectors. We present a novel active learning sampling strategy that addr…
Multi-Task Consistency for Active Learning
Aral Hekimoglu, Philipp Friedrich, Walter Zimmer +3
Learning-based solutions for vision tasks require a large amount of labeled training data to ensure their performance and reliability. In single-task vision-based settings, inconsi…