Language-Driven Active Learning for Diverse Open-Set 3D Object Detection

no code implementations • 19 Apr 2024 • Ross Greer, Bjørk Antoniussen, Andreas Møgelmose, Mohan Trivedi

In this paper, we propose VisLED, a language-driven active learning framework for diverse open-set 3D Object Detection.

3D Object Detection Active Learning +3

Raw Instinct: Trust Your Classifiers and Skip the Conversion

no code implementations • 21 Mar 2024 • Christos Kantas, Bjørk Antoniussen, Mathias V. Andersen, Rasmus Munksø, Shobhit Kotnala, Simon B. Jensen, Andreas Møgelmose, Lau Nørgaard, Thomas B. Moeslund

Using RAW-images in computer vision problems is surprisingly underexplored considering that converting from RAW to RGB does not introduce any new capture information.

ActiveAnno3D - An Active Learning Framework for Multi-Modal 3D Object Detection

1 code implementation • 5 Feb 2024 • Ahmed Ghita, Bjørk Antoniussen, Walter Zimmer, Ross Greer, Christian Creß, Andreas Møgelmose, Mohan M. Trivedi, Alois C. Knoll

We propose ActiveAnno3D, an active learning framework to select data samples for labeling that are of maximum informativeness for training.

3D Object Detection Active Learning +2

The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration

no code implementations • 30 Jan 2024 • Ross Greer, Bjørk Antoniussen, Mathias V. Andersen, Andreas Møgelmose, Mohan M. Trivedi

Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data.

3D Object Detection Active Learning +2