7 papers · 1 filter
Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels
Venkanna Babu Guthula, Oswin Krause, Dimitri Gominski +5
Supervised learning for image segmentation typically requires spatially aligned image and label sets. When images and labels originate from different sources, the pairing may be mi…
SuperF: Neural Implicit Fields for Multi-Image Super-Resolution
Sander Riisøen Jyhne, Christian Igel, Morten Goodwin +3
High-resolution imagery is often hindered by limitations in sensor technology, atmospheric conditions, and costs. Such challenges occur in satellite remote sensing, but also with h…
Taxonomy-Aware Evaluation of Vision-Language Models
Vésteinn Snæbjarnarson, Kevin Du, Niklas Stoehr +4
When a vision-language model (VLM) is prompted to identify an entity depicted in an image, it may answer 'I see a conifer,' rather than the specific label 'norway spruce'. This rai…
Familiarity-Based Open-Set Recognition Under Adversarial Attacks
Philip Enevoldsen, Christian Gundersen, Nico Lang +2
Open-set recognition (OSR), the identification of novel categories, can be a critical component when deploying classification models in real-world applications. Recent work has sho…
MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning
Vishal Nedungadi, Ankit Kariryaa, Stefan Oehmcke +3
The volume of unlabelled Earth observation (EO) data is huge, but many important applications lack labelled training data. However, EO data offers the unique opportunity to pair da…
Labeled Data Selection for Category Discovery
Bingchen Zhao, Nico Lang, Serge Belongie +1
Category discovery methods aim to find novel categories in unlabeled visual data. At training time, a set of labeled and unlabeled images are provided, where the labels correspond…