Search Results for author:
Found 25 papers, 10 papers with code
Fully Unsupervised Annotation of C. Elegans
Our fully unsupervised approach enables us to reach the accuracy of state-of-the-art supervised methodology for the use case of annotating cell nuclei in 3D microscopy images of the worm C. elegans.
Unlocking the Potential of Operations Research for Multi-Graph Matching
We consider the incomplete multi-graph matching problem, which is a generalization of the NP-hard quadratic assignment problem for matching multiple finite sets.
Unsupervised Deep Graph Matching Based on Cycle Consistency
We contribute to the sparsely populated area of unsupervised deep graph matching with application to keypoint matching in images.
Relative-Interior Solution for the (Incomplete) Linear Assignment Problem with Applications to the Quadratic Assignment Problem
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.
A Comparative Study of Graph Matching Algorithms in Computer Vision
To address these shortcomings, we present a comparative study of graph matching algorithms.
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.
Fusion Moves for Graph Matching
We contribute to approximate algorithms for the quadratic assignment problem also known as graph matching.
Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization
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
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.
A Primal-Dual Solver for Large-Scale Tracking-by-Assignment
We demonstrate the efficacy of our method on real-world tracking problems.
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.
Discrete graphical models -- an optimization perspective
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.
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.
A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems
We propose a general dual ascent framework for Lagrangean decomposition of combinatorial problems.
Global Hypothesis Generation for 6D Object Pose Estimation
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.
InstanceCut: from Edges to Instances with MultiCut
This work addresses the task of instance-aware semantic segmentation.
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization
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.
Multicuts and Perturb & MAP for Probabilistic Graph Clustering
We present a probabilistic graphical model formulation for the graph clustering problem.
Inferring M-Best Diverse Labelings in a Single One
We consider the task of finding M-best diverse solutions in a graphical model.
M-Best-Diverse Labelings for Submodular Energies and Beyond
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.
Joint Training of Generic CNN-CRF Models with Stochastic Optimization
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.
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.
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.
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
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
We consider energy minimization for undirected graphical models, also known as MAP-inference problem for Markov random fields.
