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1 code implementation • 4 Sep 2021 • Ahmed Abbas, Paul Swoboda

We propose a highly parallel primal-dual algorithm for the multicut (a. k. a.

2 code implementations • ICCV 2021 • Andrea Hornakova, Timo Kaiser, Paul Swoboda, Michal Rolinek, Bodo Rosenhahn, Roberto Henschel

We present an efficient approximate message passing solver for the lifted disjoint paths problem (LDP), a natural but NP-hard model for multiple object tracking (MOT).

2 code implementations • 6 Jun 2021 • Ahmed Abbas, Paul Swoboda

We propose a fully differentiable architecture for simultaneous semantic and instance segmentation (a. k. a.

Ranked #7 on Panoptic Segmentation on Cityscapes val

1 code implementation • ICML 2020 • Andrea Hornakova, Roberto Henschel, Bodo Rosenhahn, Paul Swoboda

We present an extension to the disjoint paths problem in which additional \emph{lifted} edges are introduced to provide path connectivity priors.

Ranked #2 on Multi-Object Tracking on 2D MOT 2015

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.

3 code implementations • 25 Mar 2020 • Michal Rolínek, Paul Swoboda, Dominik Zietlow, Anselm Paulus, Vít Musil, Georg Martius

Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers.

Ranked #3 on Graph Matching on PASCAL VOC

no code implementations • CVPR 2019 • Paul Swoboda, Dagmar Kainm"uller, Ashkan Mokarian, Christian Theobalt, Florian Bernard

We present a convex relaxation for the multi-graph matching problem.

1 code implementation • ICCV 2019 • Ahmed Abbas, Paul Swoboda

We consider general discrete Markov Random Fields(MRFs) with additional bottleneck potentials which penalize the maximum (instead of the sum) over local potential value taken by the MRF-assignment.

no code implementations • 26 Nov 2018 • Florian Bernard, Johan Thunberg, Paul Swoboda, Christian Theobalt

The matching of multiple objects (e. g. shapes or images) is a fundamental problem in vision and graphics.

1 code implementation • CVPR 2019 • Paul Swoboda, Vladimir Kolmogorov

We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference in structured energy minimization problems.

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 • Paul Swoboda, Bjoern Andres

We propose a dual decomposition and linear program relaxation of the NP -hard minimum cost multicut problem.

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 • 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 • 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.

no code implementations • 16 Jan 2013 • Paul Swoboda, Christoph Schnörr

We present a novel variational approach to image restoration (e. g., denoising, inpainting, labeling) that enables to complement established variational approaches with a histogram-based prior enforcing closeness of the solution to some given empirical measure.

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