Deep Multivariate Models with Parametric Conditionals
D. Schlesinger, B. Flach, A. Shekhovtsov · arXiv preprint 2026
Scalable Binary-Quantized Neural Networks for Energy-Efficient Vision
A. Shekhovtsov, Š. Obdržálek · ECML-PKDD 2026 · LNCS 16943, pp. 253–270
Generalized and Optimal Straight-Through Estimators
J. Hooper, A. Shekhovtsov · AISTATS · Spotlight 2026
RANSAC Scoring Functions: Analysis and Reality Check
A. Shekhovtsov · arXiv preprint 2025
Symmetric Equilibrium Learning of VAEs
B. Flach, D. Schlesinger, A. Shekhovtsov · AISTATS 2024
Cold Analysis of Rao-Blackwellized Straight-Through Gumbel-Softmax Gradient Estimator
A. Shekhovtsov · ICML 2023
Generalized Differentiable RANSAC
T. Wei, Y. Patel, A. Shekhovtsov, J. Matas, D. Barath · ICCV 2023
VAE Approximation Error: ELBO and Exponential Families
D. Schlezinger, A. Shekhovtsov, B. Flach · ICLR 2022
Bias-variance tradeoffs in single-sample binary gradient estimators
A. Shekhovtsov · GCPR 2021
Reintroducing straight-through estimators as principled methods for stochastic binary networks
A. Shekhovtsov, V. Yanush · GCPR 2021
Initialization and Transfer Learning of Stochastic Binary Networks From Real-Valued Ones
A. Livochka, A. Shekhovtsov · BiVision @ CVPR 2021
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks
A. Shekhovtsov, V. Yanush, B. Flach · NeurIPS 2020
Taxonomy of dual block-coordinate ascent methods for discrete energy minimization
S. Tourani, A. Shekhovtsov, C. Rother, B. Savchynskyy · AISTATS 2020
Belief propagation reloaded: Learning bp-layers for labeling problems
P. Knöbelreiter, C. Sormann, A. Shekhovtsov, F. Fraundorfer, T. Pock · CVPR 2020
Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers
A. Shekhovtsov, B. Flach · ICLR 2019
Stochastic Normalizations as Bayesian Learning
A. Shekhovtsov, B. Flach · ACCV 2018
Normalization of Neural Networks using Analytic Variance Propagation
A. Shekhovtsov, B. Flach · CVWW 2018
MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models
S. Tourani, A. Shekhovtsov, C. Rother, B. Savchynskyy · ECCV 2018
Feed-forward Uncertainty Propagation in Belief and Neural Networks
A. Shekhovtsov, B. Flach, M. Bušta · CoRR 2018
Generative Learning for Deep Networks
B. Flach, A. Shekhovtsov, O. Fikar · CoRR abs/1709.08524 2017
Scalable Full Flow with Learned Binary Descriptors
G. Munda, A. Shekhovtsov, P. Knöbelreiter, T. Pock · GCPR 2017
Maximum Persistency via Iterative Relaxed Inference with Graphical Models
A. Shekhovtsov, P. Swoboda, B. Savchynskyy · PAMI 2017
End-to-End Training of Hybrid CNN-CRF Models for Stereo
P. Knöbelreiter, C. Reinbacher, A. Shekhovtsov, T. Pock · CVPR 2017
Complexity of Discrete Energy Minimization Problems
M. Li, A. Shekhovtsov, D. Huber · ECCV 2016
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization
A. Kirillov, A. Shekhovtsov, C. Rother, B. Savchynskyy · NIPS 2016
Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization
A. Shekhovtsov, C. Reinbacher, G. Graber, T. Pock · CVWW 2016
Maximum Persistency via Iterative Relaxed Inference with Graphical Models
A. Shekhovtsov, P. Swoboda, B. Savchynskyy · CVPR 2015
Higher Order Maximum Persistency and Comparison Theorems
A. Shekhovtsov · CVIU (SI on Inference & Learning of Graphical Models) 2014
Maximum Persistency in Energy Minimization
A. Shekhovtsov · CVPR 2014
Exact and Partial Energy Minimization in Computer Vision
A. Shekhovtsov · PhD Thesis, CTU in Prague 2013
Curvature Prior for MRF-Based Segmentation and Shape Inpainting
A. Shekhovtsov, P. Kohli, C. Rother · DAGM/OAGM 2012
A Distributed Mincut/Maxflow Algorithm Combining Path Augmentation and Push-Relabel
A. Shekhovtsov, V. Hlaváč · IJCV 2012
On Partial Optimality by Auxiliary Submodular Problems
A. Shekhovtsov, V. Hlaváč · Control Systems and Computers, 2011(2), special issue 2011
Joint Image GMM and Shading MAP Estimation
A. Shekhovtsov, V. Hlaváč · ICPR 2010
A Lower Bound by One-against-all Decomposition for Potts Model Energy Minimization
A. Shekhovtsov, V. Hlaváč · Computer Vision Winter Workshop 2008
On Partial Optimality in Multi-label MRFs
P. Kohli, A. Shekhovtsov, C. Rother, V. Kolmogorov, P. Torr · ICML 2008
A Discrete Search Method for Multi-modal Non-Rigid Image Registration
A. Shekhovtsov, J.D. Garcia-Arteaga, T. Werner · NORDIA workshop @ CVPR 2008
Efficient MRF Deformation Model for Non-Rigid Image Matching
A. Shekhovtsov, I. Kovtun, V. Hlaváč · Computer Vision and Image Understanding 2008
Unified Framework for Semiring-Based Arc Consistency and Relaxation Labeling
T. Werner, A. Shekhovtsov · Computer Vision Winter Workshop, St. Lambrecht, Austria 2007
Supermodular Decomposition of Structural Labeling Problem
A. Shekhovtsov · Control Systems and Computers #1, 2006, pp.39-48 (in Russian) 2006
A Higher Order MRF-Model for Stereo-Reconstruction
B. Flach, D. Schlesinger, A. Shekhovtsov · Pattern Recognition, LNCS vol. 3175, pp. 440–446 2004