2 citations · 3 across the 8 of their papers we have counts for
3 papers · 1 filter
Two Global Crops Suffice: Locating Semantic Emergence in DINO-Style Self-Supervised Learning
Basavaraj Sunagad, Artur Jesslen, Adam Kortylewski
Self-supervised vision transformers trained with DINO-style objectives exhibit striking emergent semantic representation quality across visual tasks, yet the mechanisms underlying…
Geometry Matters: 3D Foundation Priors for Learning Semantic Correspondence
Artur Jesslen, Olaf Dünkel, Adam Kortylewski
Foundation features from self-supervised vision models and text-to-image diffusion models have proven effective for semantic correspondence estimation. However, because these featu…
Category-Level 3D Correspondence in Camera Space via Morphable Object Priors
Leonhard Sommer, Artur Jesslen, Basavaraj Sunagad +1
Understanding 3D objects from images is fundamental to robotics and AR/VR applications. While recent work has made progress in category-level pose estimation, current representatio…