Yuri Boykov | DeepAI

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∙ 03/13/2023

Collision Cross-entropy and EM Algorithm for Self-labeled Classification

We propose "collision cross-entropy" as a robust alternative to the Shan...

0 Zhongwen Zhang, et al. ∙

∙ 01/26/2023

Revisiting Discriminative Entropy Clustering and its relation to K-means

Maximization of mutual information between the model's input and output ...

0 Zhongwen Zhang, et al. ∙

∙ 04/05/2021

Robust Trust Region for Weakly Supervised Segmentation

Acquisition of training data for the standard semantic segmentation is e...

0 Dmitrii Marin, et al. ∙

∙ 03/26/2021

Confluent Vessel Trees with Accurate Bifurcations

We are interested in unsupervised reconstruction of complex near-capilla...

0 Zhongwen Zhang, et al. ∙

∙ 02/10/2020

RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation

We propose a novel efficient and lightweight model for human pose estima...

56 Hossam Isack, et al. ∙

∙ 01/15/2020

Image Segmentation Using Deep Learning: A Survey

Image segmentation is a key topic in image processing and computer visio...

163 Shervin Minaee, et al. ∙

∙ 07/16/2019

Efficient Segmentation: Learning Downsampling Near Semantic Boundaries

Many automated processes such as auto-piloting rely on a good semantic s...

1 Dmitrii Marin, et al. ∙

∙ 11/24/2018

Divergence Prior and Vessel-tree Reconstruction

We propose a new geometric regularization principle for reconstructing v...

0 Zhongwen Zhang, et al. ∙

∙ 09/07/2018

ADM for grid CRF loss in CNN segmentation

Variants of gradient descent (GD) dominate CNN loss minimization in comp...

0 Dmitrii Marin, et al. ∙

∙ 05/12/2018

Constrained-CNN losses forweakly supervised segmentation

Weak supervision, e.g., in the form of partial labels or image tags, is ...

0 Hoel Kervadec, et al. ∙

∙ 04/04/2018

Normalized Cut Loss for Weakly-supervised CNN Segmentation

Most recent semantic segmentation methods train deep convolutional neura...

2 Meng Tang, et al. ∙

∙ 03/26/2018

On Regularized Losses for Weakly-supervised CNN Segmentation

Minimization of regularized losses is a principled approach to weak supe...

0 Meng Tang, et al. ∙

∙ 05/16/2017

Kernel clustering: density biases and solutions

Kernel methods are popular in clustering due to their generality and dis...

0 Dmitrii Marin, et al. ∙

∙ 03/30/2017

Efficient optimization for Hierarchically-structured Interacting Segments (HINTS)

We propose an effective optimization algorithm for a general hierarchica...

0 Hossam Isack, et al. ∙

∙ 02/02/2016

A-expansion for multiple "hedgehog" shapes

Overlapping colors and cluttered or weak edges are common segmentation p...

0 Hossam Isack, et al. ∙

∙ 06/24/2015

Kernel Cuts: MRF meets Kernel & Spectral Clustering

We propose a new segmentation model combining common regularization ener...

0 Meng Tang, et al. ∙

∙ 06/15/2015

Thin Structure Estimation with Curvature Regularization

Many applications in vision require estimation of thin structures such a...

0 Dmitrii Marin, et al. ∙

∙ 05/01/2015

Volumetric Bias in Segmentation and Reconstruction: Secrets and Solutions

Many standard optimization methods for segmentation and reconstruction c...

0 Yuri Boykov, et al. ∙

∙ 07/23/2014

Joint Energy-based Detection and Classificationon of Multilingual Text Lines

This paper proposes a new hierarchical MDL-based model for a joint detec...

0 Igor Milevskiy, et al. ∙

∙ 11/08/2013

An Experimental Comparison of Trust Region and Level Sets

High-order (non-linear) functionals have become very popular in segmenta...

0 Lena Gorelick, et al. ∙

∙ 11/08/2013

Submodularization for Quadratic Pseudo-Boolean Optimization

Many computer vision problems require optimization of binary non-submodu...

0 Lena Gorelick, et al. ∙

∙ 11/07/2013

Efficient Regularization of Squared Curvature

Curvature has received increased attention as an important alternative t...

0 Claudia Nieuwenhuis, et al. ∙

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