17 citations · 44 across the 6 of their papers we have counts for
7 papers · 1 filter
Non-isotropy Regularization for Proxy-based Deep Metric Learning
Karsten Roth, Oriol Vinyals, Zeynep Akata
Deep Metric Learning (DML) aims to learn representation spaces on which semantic relations can simply be expressed through predefined distance metrics. Best performing approaches c…
Integrating Language Guidance into Vision-based Deep Metric Learning
Karsten Roth, Oriol Vinyals, Zeynep Akata
Deep Metric Learning (DML) proposes to learn metric spaces which encode semantic similarities as embedding space distances. These spaces should be transferable to classes beyond th…
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…
MIC: Mining Interclass Characteristics for Improved Metric Learning
Karsten Roth, Biagio Brattoli, Björn Ommer
Metric learning seeks to embed images of objects suchthat class-defined relations are captured by the embeddingspace. However, variability in images is not just due to different de…