17 citations · 38 across the 17 of their papers we have counts for
8 papers · 1 filter
Foundation Models for Video Understanding: A Survey
Neelu Madan, Andreas Moegelmose, Rajat Modi +2
Video Foundation Models (ViFMs) aim to learn a general-purpose representation for various video understanding tasks. Leveraging large-scale datasets and powerful models, ViFMs achi…
Driver Activity Classification Using Generalizable Representations from Vision-Language Models
Ross Greer, Mathias Viborg Andersen, Andreas Møgelmose +1
Driver activity classification is crucial for ensuring road safety, with applications ranging from driver assistance systems to autonomous vehicle control transitions. In this pape…
OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilities
Lasse H. Hansen, Simon B. Jensen, Mark P. Philipsen +3
Identifying and classifying underground utilities is an important task for efficient and effective urban planning and infrastructure maintenance. We present OpenTrench3D, a novel a…
Language-Driven Active Learning for Diverse Open-Set 3D Object Detection
Ross Greer, Bjørk Antoniussen, Andreas Møgelmose +1
Object detection is crucial for ensuring safe autonomous driving. However, data-driven approaches face challenges when encountering minority or novel objects in the 3D driving scen…
Raw Instinct: Trust Your Classifiers and Skip the Conversion
Christos Kantas, Bjørk Antoniussen, Mathias V. Andersen +6
Using RAW-images in computer vision problems is surprisingly underexplored considering that converting from RAW to RGB does not introduce any new capture information. In this paper…
Learning to Find Missing Video Frames with Synthetic Data Augmentation: A General Framework and Application in Generating Thermal Images Using RGB Cameras
Mathias Viborg Andersen, Ross Greer, Andreas Møgelmose +1
Advanced Driver Assistance Systems (ADAS) in intelligent vehicles rely on accurate driver perception within the vehicle cabin, often leveraging a combination of sensing modalities.…
ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection
Ahmed Ghita, Bjørk Antoniussen, Walter Zimmer +5
The curation of large-scale datasets is still costly and requires much time and resources. Data is often manually labeled, and the challenge of creating high-quality datasets remai…
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration
Ross Greer, Bjørk Antoniussen, Mathias V. Andersen +2
Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data. We demons…