4 papers
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…