736 citations · 1.1k across the 52 of their papers we have counts for
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Cross and Learn: Cross-Modal Self-Supervision
Nawid Sayed, Biagio Brattoli, Björn Ommer
In this paper we present a self-supervised method for representation learning utilizing two different modalities. Based on the observation that cross-modal information has a high s…
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning
Uta Büchler, Biagio Brattoli, Björn Ommer
Self-supervised learning of convolutional neural networks can harness large amounts of cheap unlabeled data to train powerful feature representations. As surrogate task, we jointly…
A Style-Aware Content Loss for Real-time HD Style Transfer
Artsiom Sanakoyeu, Dmytro Kotovenko, Sabine Lang +1
Recently, style transfer has received a lot of attention. While much of this research has aimed at speeding up processing, the approaches are still lacking from a principled, art h…
A Variational U-Net for Conditional Appearance and Shape Generation
Patrick Esser, Ekaterina Sutter, Björn Ommer
Deep generative models have demonstrated great performance in image synthesis. However, results deteriorate in case of spatial deformations, since they generate images of objects d…
Deep Unsupervised Learning of Visual Similarities
Artsiom Sanakoyeu, Miguel A. Bautista, Björn Ommer
Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision. In this context, however, the recent breakthrough in dee…