7 papers
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…
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…
PADS: Policy-Adapted Sampling for Visual Similarity Learning
Karsten Roth, Timo Milbich, Björn Ommer
Learning visual similarity requires to learn relations, typically between triplets of images. Albeit triplet approaches being powerful, their computational complexity mostly limits…
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Karsten Roth, Timo Milbich, Samarth Sinha +3
Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year. Although the field b…
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…
Unsupervised Part-Based Disentangling of Object Shape and Appearance
Dominik Lorenz, Leonard Bereska, Timo Milbich +1
Large intra-class variation is the result of changes in multiple object characteristics. Images, however, only show the superposition of different variable factors such as appearan…