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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…
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…
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…