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no code implementations • 18 Jul 2023 • Siddharth Tourani, Carsten Rother, Muhammad Haris Khan, Bogdan Savchynskyy

We contribute to the sparsely populated area of unsupervised deep graph matching with application to keypoint matching in images.

no code implementations • 26 Jan 2023 • Tomáš Dlask, Bogdan Savchynskyy

We study the set of optimal solutions of the dual linear programming formulation of the linear assignment problem (LAP) to propose a method for computing a solution from the relative interior of this set.

1 code implementation • 1 Jul 2022 • Stefan Haller, Lorenz Feineis, Lisa Hutschenreiter, Florian Bernard, Carsten Rother, Dagmar Kainmüller, Paul Swoboda, Bogdan Savchynskyy

To address these shortcomings, we present a comparative study of graph matching algorithms.

no code implementations • 4 Feb 2022 • Paul Swoboda, Bjoern Andres, Andrea Hornakova, Florian Bernard, Jannik Irmai, Paul Roetzer, Bogdan Savchynskyy, David Stein, Ahmed Abbas

In order to facilitate algorithm development for their numerical solution, we collect in one place a large number of datasets in easy to read formats for a diverse set of problem classes.

1 code implementation • ICCV 2021 • Lisa Hutschenreiter, Stefan Haller, Lorenz Feineis, Carsten Rother, Dagmar Kainmüller, Bogdan Savchynskyy

We contribute to approximate algorithms for the quadratic assignment problem also known as graph matching.

1 code implementation • 16 Apr 2020 • Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy

We consider the maximum-a-posteriori inference problem in discrete graphical models and study solvers based on the dual block-coordinate ascent rule.

no code implementations • ECCV 2018 • Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy

Dense, discrete Graphical Models with pairwise potentials are a powerful class of models which are employed in state-of-the-art computer vision and bio-imaging applications.

1 code implementation • 14 Apr 2020 • Stefan Haller, Mangal Prakash, Lisa Hutschenreiter, Tobias Pietzsch, Carsten Rother, Florian Jug, Paul Swoboda, Bogdan Savchynskyy

We demonstrate the efficacy of our method on real-world tracking problems.

1 code implementation • 14 Apr 2020 • Stefan Haller, Paul Swoboda, Bogdan Savchynskyy

This property allows to significantly reduce the computational time of the combinatorial solver and therefore solve problems which were out of reach before.

no code implementations • 24 Jan 2020 • Bogdan Savchynskyy

The monograph can be useful for undergraduate and graduate students studying optimization or graphical models, as well as for experts in optimization who want to have a look into graphical models.

1 code implementation • CVPR 2017 • Paul Swoboda, Carsten Rother, Hassan Abu Alhaija, Dagmar Kainmueller, Bogdan Savchynskyy

We study the quadratic assignment problem, in computer vision also known as graph matching.

1 code implementation • CVPR 2017 • Paul Swoboda, Jan Kuske, Bogdan Savchynskyy

We propose a general dual ascent framework for Lagrangean decomposition of combinatorial problems.

no code implementations • CVPR 2017 • Frank Michel, Alexander Kirillov, Eric Brachmann, Alexander Krull, Stefan Gumhold, Bogdan Savchynskyy, Carsten Rother

Most modern approaches solve this task in three steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii) Select and refine a pose from the pool.

no code implementations • CVPR 2017 • Alexander Kirillov, Evgeny Levinkov, Bjoern Andres, Bogdan Savchynskyy, Carsten Rother

This work addresses the task of instance-aware semantic segmentation.

no code implementations • NeurIPS 2016 • Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy

In particular, the joint M-best diverse labelings can be obtained by running a non-parametric submodular minimization (in the special case - max-flow) solver for M different values of $\gamma$ in parallel, for certain diversity measures.

no code implementations • 9 Jan 2016 • Jörg Hendrik Kappes, Paul Swoboda, Bogdan Savchynskyy, Tamir Hazan, Christoph Schnörr

We present a probabilistic graphical model formulation for the graph clustering problem.

no code implementations • NeurIPS 2015 • Alexander Kirillov, Dmytro Shlezinger, Dmitry P. Vetrov, Carsten Rother, Bogdan Savchynskyy

In this work we show that the joint inference of $M$ best diverse solutions can be formulated as a submodular energy minimization if the original MAP-inference problem is submodular, hence fast inference techniques can be used.

no code implementations • ICCV 2015 • Alexander Kirillov, Bogdan Savchynskyy, Dmitrij Schlesinger, Dmitry Vetrov, Carsten Rother

We consider the task of finding M-best diverse solutions in a graphical model.

no code implementations • 16 Nov 2015 • Alexander Kirillov, Dmitrij Schlesinger, Shuai Zheng, Bogdan Savchynskyy, Philip H. S. Torr, Carsten Rother

We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Random Field (CRF) parameters.

no code implementations • CVPR 2015 • Alexander Shekhovtsov, Paul Swoboda, Bogdan Savchynskyy

We propose an efficient implementation, which runs in time comparable to a single run of a suboptimal dual solver.

no code implementations • CVPR 2014 • Paul Swoboda, Alexander Shekhovtsov, Jörg Hendrik Kappes, Christoph Schnörr, Bogdan Savchynskyy

We propose a novel polynomial time algorithm to obtain a part of its optimal non-relaxed integral solution.

no code implementations • 2 Apr 2014 • Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother

However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.

no code implementations • NeurIPS 2013 • Bogdan Savchynskyy, Jörg Hendrik Kappes, Paul Swoboda, Christoph Schnörr

We consider energy minimization for undirected graphical models, also known as MAP-inference problem for Markov random fields.

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