Search Results for author: Bogdan Savchynskyy

Found 23 papers, 7 papers with code

Unsupervised Deep Graph Matching Based on Cycle Consistency

no code implementations18 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.

Graph Matching

Relative-Interior Solution for (Incomplete) Linear Assignment Problem with Applications to Quadratic Assignment Problem

no code implementations26 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.

Structured Prediction Problem Archive

no code implementations4 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.

Benchmarking Structured Prediction

Fusion Moves for Graph Matching

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.

Graph Matching

Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization

1 code implementation16 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.

MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models

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.

6D Pose Estimation using RGB

Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation

1 code implementation14 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.

Discrete graphical models -- an optimization perspective

no code implementations24 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.

Combinatorial Optimization

Global Hypothesis Generation for 6D Object Pose Estimation

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.

6D Pose Estimation using RGB

Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization

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.

M-Best-Diverse Labelings for Submodular Energies and Beyond

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.

Total Energy

Joint Training of Generic CNN-CRF Models with Stochastic Optimization

no code implementations16 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.

Stochastic Optimization

Maximum Persistency via Iterative Relaxed Inference with Graphical Models

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.

A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems

no code implementations2 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.

Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation

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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