207 citations · 264 across the 10 of their papers we have counts for
14 papers
Adiabatic Quantum Graph Matching with Permutation Matrix Constraints
Marcel Seelbach Benkner, Vladislav Golyanik, Christian Theobalt +1
Matching problems on 3D shapes and images are challenging as they are frequently formulated as combinatorial quadratic assignment problems (QAPs) with permutation matrix constraint…
Q-Match: Iterative Shape Matching via Quantum Annealing
Marcel Seelbach Benkner, Zorah Lähner, Vladislav Golyanik +3
Finding shape correspondences can be formulated as an NP-hard quadratic assignment problem (QAP) that becomes infeasible for shapes with high sampling density. A promising research…
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…
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching
Jonas Geiping, Liam Fowl, W. Ronny Huang +4
Data Poisoning attacks modify training data to maliciously control a model trained on such data. In this work, we focus on targeted poisoning attacks which cause a reclassification…
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…
Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory
Micah Goldblum, Jonas Geiping, Avi Schwarzschild +2
We empirically evaluate common assumptions about neural networks that are widely held by practitioners and theorists alike. In this work, we: (1) prove the widespread existence of…
Parametric Majorization for Data-Driven Energy Minimization Methods
Jonas Geiping, Michael Moeller
Energy minimization methods are a classical tool in a multitude of computer vision applications. While they are interpretable and well-studied, their regularity assumptions are dif…
Convolutional Simplex Projection Network (CSPN) for Weakly Supervised Semantic Segmentation
Rania Briq, Michael Moeller, Juergen Gall
Weakly supervised semantic segmentation has been a subject of increased interest due to the scarcity of fully annotated images. We introduce a new approach for solving weakly super…
Are good local minima wide in sparse recovery?
Michael Moeller, Otmar Loffeld, Juergen Gall +1
The idea of compressed sensing is to exploit representations in suitable (overcomplete) dictionaries that allow to recover signals far beyond the Nyquist rate provided that they ad…
Locally Sparse Reconstruction Using the -Norm
Pia Heins, Michael Moeller, Martin Burger
This paper discusses the incorporation of local sparsity information, e.g. in each pixel of an image, via minimization of the -norm. We discuss the basic propertie…