Publications (17)
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
Timo Milbich, Karsten Roth, Samarth Sinha +3
Deep Metric Learning (DML) aims to find representations suitable for zero-shot transfer to a priori unknown test distributions. However, common evaluation protocols only test a sin…
Unsupervised Representation Learning by Discovering Reliable Image Relations
Timo Milbich, Omair Ghori, Ferran Diego +1
Learning robust representations that allow to reliably establish relations between images is of paramount importance for virtually all of computer vision. Annotating the quadratic…
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Timo Milbich, Karsten Roth, Homanga Bharadhwaj +4
Visual Similarity plays an important role in many computer vision applications. Deep metric learning (DML) is a powerful framework for learning such similarities which not only gen…
Understanding Object Dynamics for Interactive Image-to-Video Synthesis
Andreas Blattmann, Timo Milbich, Michael Dorkenwald +1
What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level…
Behavior-Driven Synthesis of Human Dynamics
Andreas Blattmann, Timo Milbich, Michael Dorkenwald +1
Generating and representing human behavior are of major importance for various computer vision applications. Commonly, human video synthesis represents behavior as sequences of pos…
Mind the Gap: Continuous Magnification Sampling for Pathology Foundation Models
Alexander Möllers, Julius Hense, Florian Schulz +3
In histopathology, pathologists examine both tissue architecture at low magnification and fine-grained morphology at high magnification. Yet, the performance of pathology foundatio…