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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 Robust Disentangling of Latent Characteristics for Image Synthesis
Patrick Esser, Johannes Haux, Björn Ommer
Deep generative models come with the promise to learn an explainable representation for visual objects that allows image sampling, synthesis, and selective modification. The main c…
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…
Multi-Scale Convolutions for Learning Context Aware Feature Representations
Nikolai Ufer, Kam To Lui, Katja Schwarz +2
Finding semantic correspondences is a challenging problem. With the breakthrough of CNNs stronger features are available for tasks like classification but not specifically for the…
Divide and Conquer the Embedding Space for Metric Learning
Artsiom Sanakoyeu, Vadim Tschernezki, Uta Büchler +1
Learning the embedding space, where semantically similar objects are located close together and dissimilar objects far apart, is a cornerstone of many computer vision applications.…
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…