8 papers
Nonlinear Spectral Geometry Processing via the TV Transform
Marco Fumero, Michael Moeller, Emanuele Rodolà
We introduce a novel computational framework for digital geometry processing, based upon the derivation of a nonlinear operator associated to the total variation functional. Such o…
Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction
Christina Runkel, Stefan Dorenkamp, Hartmut Bauermeister +1
Machine learning techniques have excelled in the automatic semantic analysis of images, reaching human-level performances on challenging benchmarks. Yet, the semantic analysis of v…
A Simple Domain Shifting Networkfor Generating Low Quality Images
Guruprasad Hegde, Avinash Nittur Ramesh, Kanchana Vaishnavi Gandikota +2
Deep Learning systems have proven to be extremely successful for image recognition tasks for which significant amounts of training data is available, e.g., on the famous ImageNet d…
A Generative Model for Generic Light Field Reconstruction
Paramanand Chandramouli, Kanchana Vaishnavi Gandikota, Andreas Goerlitz +2
Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time a generative model for…
Fast Convex Relaxations using Graph Discretizations
Jonas Geiping, Fjedor Gaede, Hartmut Bauermeister +1
Matching and partitioning problems are fundamentals of computer vision applications with examples in multilabel segmentation, stereo estimation and optical-flow computation. These…
Inverting Gradients -- How easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge +1
The idea of federated learning is to collaboratively train a neural network on a server. Each user receives the current weights of the network and in turns sends parameter updates…