1 citations · 1 across the 7 of their papers we have counts for
8 papers
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…
Emergence of a Shared Canonical Object Frame from In-the-Wild Videos
Tom Fischer, Martin Sundermeyer, Adam Kortylewski +1
Comparing object orientations and positions across different instances requires their poses to be expressed in a shared canonical frame. Establishing such frames has traditionally…
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…
Every9D-21M: Large-Scale Real-World 9D Canonicalization of Everyday Objects
Leonhard Sommer, Emil Akopyan, Adam Kortylewski
Estimating the 9D pose of everyday objects from a single real-world image remains challenging. This is largely due to the lack of large-scale supervision. Most existing datasets ei…
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…
SEMAGIC: Learning Semantically Consistent Deformable 3D Representations from In-the-Wild Images
Sky Cen, Wufei Ma, Guofeng Zhang +2
Learning deformable 3D object models from single-view in-the-wild images has enabled impressive 3D shape reconstruction without supervision. However, it remains unclear whether the…
PASR: Pose-Aware 3D Shape Retrieval from Occluded Single Views
Jiaxin Shi, Guofeng Zhang, Wufei Ma +3
Single-view 3D shape retrieval is a fundamental yet challenging task that is increasingly important with the growth of available 3D data. Existing approaches largely fall into two…
SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding
Ilke Demir, Camilla Hahn, Kathryn Leonard +7
We present SkelNetOn 2019 Challenge and Deep Learning for Geometric Shape Understanding workshop to utilize existing and develop novel deep learning architectures for shape underst…