84 citations · 213 across the 28 of their papers we have counts for
18 papers · 1 filter
DiscoMatch: Fast Discrete Optimisation for Geometrically Consistent 3D Shape Matching
Paul Roetzer, Ahmed Abbas, Dongliang Cao +2
In this work we propose to combine the advantages of learningbased and combinatorial formalisms for 3D shape matching. While learningbased methods lead to state-of-the-art matching…
A Multidimensional Analysis of Social Biases in Vision Transformers
Jannik Brinkmann, Paul Swoboda, Christian Bartelt
The embedding spaces of image models have been shown to encode a range of social biases such as racism and sexism. Here, we investigate specific factors that contribute to the emer…
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
Duy M. H. Nguyen, Hoang Nguyen, Nghiem T. Diep +9
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained dee…
ClusterFuG: Clustering Fully connected Graphs by Multicut
Ahmed Abbas, Paul Swoboda
We propose a graph clustering formulation based on multicut (a.k.a. weighted correlation clustering) on the complete graph. Our formulation does not need specification of the graph…
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering
Duy M. H. Nguyen, Hoang Nguyen, Mai T. N. Truong +7
Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs i…
A Comparative Study of Graph Matching Algorithms in Computer Vision
Stefan Haller, Lorenz Feineis, Lisa Hutschenreiter +5
The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide ra…
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching
Paul Roetzer, Paul Swoboda, Daniel Cremers +1
We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes. We use the mathematically elegant forma…
LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking
Duy M. H. Nguyen, Roberto Henschel, Bodo Rosenhahn +2
Multi-Camera Multi-Object Tracking is currently drawing attention in the computer vision field due to its superior performance in real-world applications such as video surveillance…
Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths
Andrea Hornakova, Timo Kaiser, Paul Swoboda +3
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). Our tracker…
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach
Ahmed Abbas, Paul Swoboda
We propose a fully differentiable architecture for simultaneous semantic and instance segmentation (a.k.a. panoptic segmentation) consisting of a convolutional neural network and a…
Lifted Disjoint Paths with Application in Multiple Object Tracking
Andrea Hornakova, Roberto Henschel, Bodo Rosenhahn +1
We present an extension to the disjoint paths problem in which additional \emph{lifted} edges are introduced to provide path connectivity priors. We call the resulting optimization…
A Primal-Dual Solver for Large-Scale Tracking-by-Assignment
Stefan Haller, Mangal Prakash, Lisa Hutschenreiter +5
We propose a fast approximate solver for the combinatorial problem known as tracking-by-assignment, which we apply to cell tracking. The latter plays a key role in discovery in man…
Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation
Stefan Haller, Paul Swoboda, Bogdan Savchynskyy
We consider the MAP-inference problem for graphical models, which is a valued constraint satisfaction problem defined on real numbers with a natural summation operation. We propose…
Bottleneck potentials in Markov Random Fields
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 th…
Higher-order Projected Power Iterations for Scalable Multi-Matching
Florian Bernard, Johan Thunberg, Paul Swoboda +1
The matching of multiple objects (e.g. shapes or images) is a fundamental problem in vision and graphics. In order to robustly handle ambiguities, noise and repetitive patterns in…
A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching
Paul Swoboda, Carsten Rother, Hassan Abu Alhaija +2
We study the quadratic assignment problem, in computer vision also known as graph matching. Two leading solvers for this problem optimize the Lagrange decomposition duals with sub-…
Multicuts and Perturb & MAP for Probabilistic Graph Clustering
Jörg Hendrik Kappes, Paul Swoboda, Bogdan Savchynskyy +2
We present a probabilistic graphical model formulation for the graph clustering problem. This enables to locally represent uncertainty of image partitions by approximate marginal d…
Maximum Persistency via Iterative Relaxed Inference with Graphical Models
Alexander Shekhovtsov, Paul Swoboda, Bogdan Savchynskyy
We consider the NP-hard problem of MAP-inference for undirected discrete graphical models. We propose a polynomial time and practically efficient algorithm for finding a part of it…