Search Results for author:
Found 31 papers, 17 papers with code
Convex Variational Image Restoration with Histogram Priors
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.
Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation
We consider energy minimization for undirected graphical models, also known as MAP-inference problem for Markov random fields.
Partial Optimality by Pruning for MAP-Inference with General Graphical Models
We propose a novel polynomial time algorithm to obtain a part of its optimal non-relaxed integral solution.
Maximum Persistency via Iterative Relaxed Inference with Graphical Models
We propose an efficient implementation, which runs in time comparable to a single run of a suboptimal dual solver.
Multicuts and Perturb & MAP for Probabilistic Graph Clustering
We present a probabilistic graphical model formulation for the graph clustering problem.
A Message Passing Algorithm for the Minimum Cost Multicut Problem
We propose a dual decomposition and linear program relaxation of the NP -hard minimum cost multicut problem.
A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems
We propose a general dual ascent framework for Lagrangean decomposition of combinatorial problems.
A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching
We study the quadratic assignment problem, in computer vision also known as graph matching.
MAP inference via Block-Coordinate Frank-Wolfe Algorithm
We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference in structured energy minimization problems.
Higher-order Projected Power Iterations for Scalable Multi-Matching
The matching of multiple objects (e. g. shapes or images) is a fundamental problem in vision and graphics.
Bottleneck potentials in Markov Random Fields
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.
A Convex Relaxation for Multi-Graph Matching
We present a convex relaxation for the multi-graph matching problem.
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
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 #2 on
Graph Matching
on PASCAL VOC
Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation
This property allows to significantly reduce the computational time of the combinatorial solver and therefore solve problems which were out of reach before.
A Primal-Dual Solver for Large-Scale Tracking-by-Assignment
We demonstrate the efficacy of our method on real-world tracking problems.
Lifted Disjoint Paths with Application in Multiple Object Tracking
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
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach
We propose a fully differentiable architecture for simultaneous semantic and instance segmentation (a. k. a.
Ranked #8 on
Panoptic Segmentation
on Cityscapes test
Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths
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).
RAMA: A Rapid Multicut Algorithm on GPU
We propose a highly parallel primal-dual algorithm for the multicut (a. k. a.
FastDOG: Fast Discrete Optimization on GPU
We present a massively parallel Lagrange decomposition method for solving 0--1 integer linear programs occurring in structured prediction.
LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking
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 in crowded scenes or in wide spaces.
Structured Prediction Problem Archive
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.
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching
We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes.
DOGE-Train: Discrete Optimization on GPU with End-to-end Training
Our solver achieves significantly faster performance and better dual objectives than its non-learned version, achieving close to optimal objective values of LP relaxations of very large structured prediction problems and on selected combinatorial ones.
A Comparative Study of Graph Matching Algorithms in Computer Vision
To address these shortcomings, we present a comparative study of graph matching algorithms.
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering
Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data.
ClusterFuG: Clustering Fully connected Graphs by Multicut
We propose a graph clustering formulation based on multicut (a. k. a.
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images.
A Multidimensional Analysis of Social Biases in Vision Transformers
Therefore, we measure the impact of training data, model architecture, and training objectives on social biases in the learned representations of ViTs.
Fast Discrete Optimisation for Geometrically Consistent 3D Shape Matching
In this work we propose to combine the advantages of learning-based and combinatorial formalisms for 3D shape matching.
A Mechanistic Analysis of a Transformer Trained on a Symbolic Multi-Step Reasoning Task
We anticipate that the motifs we identified in our synthetic setting can provide valuable insights into the broader operating principles of transformers and thus provide a basis for understanding more complex models.
