Nicolas Papadakis

research

∙ 09/19/2023

Single-Image based unsupervised joint segmentation and denoising

In this work, we develop an unsupervised method for the joint segmentati...

0 Nadja Gruber, et al. ∙

research

∙ 06/06/2023

Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems

Plug-and-Play (PnP) methods are efficient iterative algorithms for solvi...

0 Samuel Hurault, et al. ∙

research

∙ 03/20/2023

Inverse problem regularization with hierarchical variational autoencoders

In this paper, we propose to regularize ill-posed inverse problems using...

0 Jean Prost, et al. ∙

research

∙ 01/31/2023

A relaxed proximal gradient descent algorithm for convergent plug-and-play with proximal denoiser

This paper presents a new convergent Plug-and-Play (PnP) algorithm. PnP ...

0 Samuel Hurault, et al. ∙

research

∙ 11/13/2022

SCOTCH and SODA: A Transformer Video Shadow Detection Framework

Shadows in videos are difficult to detect because of the large shadow de...

0 Lihao Liu, et al. ∙

research

∙ 09/14/2022

A patch-based architecture for multi-label classification from single label annotations

In this paper, we propose a patch-based architecture for multi-label cla...

0 Warren Jouanneau, et al. ∙

research

∙ 05/20/2022

Diverse super-resolution with pretrained deep hiererarchical VAEs

Image super-resolution is a one-to-many problem, but most deep-learning ...

0 Jean Prost, et al. ∙

research

∙ 04/04/2022

Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification

The automatic early diagnosis of prodromal stages of Alzheimer's disease...

0 Angelica I. Aviles-Rivero, et al. ∙

research

∙ 01/31/2022

Proximal denoiser for convergent plug-and-play optimization with nonconvex regularization

Plug-and-Play (PnP) methods solve ill-posed inverse problems through ite...

5 Samuel Hurault, et al. ∙

research

∙ 10/07/2021

Gradient Step Denoiser for convergent Plug-and-Play

Plug-and-Play methods constitute a class of iterative algorithms for ima...

10 Samuel Hurault, et al. ∙

research

∙ 09/13/2021

POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring

Semi-supervised learning (SSL) uses unlabeled data to compensate for the...

0 Reda Abdellah Kamraoui, et al. ∙

research

∙ 02/11/2021

Learning local regularization for variational image restoration

In this work, we propose a framework to learn a local regularization mod...

0 Jean Prost, et al. ∙

research

∙ 02/10/2021

On the Existence of Optimal Transport Gradient for Learning Generative Models

The use of optimal transport cost for learning generative models has bec...

0 Antoine Houdard, et al. ∙

research

∙ 09/30/2020

GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays

Can one learn to diagnose COVID-19 under extreme minimal supervision? Si...

0 Angelica I. Aviles-Rivero, et al. ∙

research

∙ 05/26/2020

Multi-task deep learning for image segmentation using recursive approximation tasks

Fully supervised deep neural networks for segmentation usually require a...

58 Rihuan Ke, et al. ∙

research

∙ 10/04/2019

Variational Osmosis for Non-linear Image Fusion

We propose a new variational model for nonlinear image fusion. Our appro...

5 Simone Parisotto, et al. ∙

research

∙ 07/23/2019

GraphX^NET- Chest X-Ray Classification Under Extreme Minimal Supervision

The task of classifying X-ray data is a problem of both theoretical and ...

8 Angelica I. Aviles-Rivero, et al. ∙

research

∙ 06/20/2019

A multi-task U-net for segmentation with lazy labels

The need for labour intensive pixel-wise annotation is a major limitatio...

0 Rihuan Ke, et al. ∙

research

∙ 06/20/2019

Beyond Supervised Classification: Extreme Minimal Supervision with the Graph 1-Laplacian

We consider the task of classifying when an extremely reduced amount of ...

0 Angelica I. Aviles-Rivero, et al. ∙

research

∙ 03/17/2019

Robust superpixels using color and contour features along linear path

Superpixel decomposition methods are widely used in computer vision and ...

2 Remi Giraud, et al. ∙

research

∙ 03/17/2019

SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches

Superpixels have become very popular in many computer vision application...

6 Remi Giraud, et al. ∙

research

∙ 03/17/2019

An Optimized PatchMatch for Multi-scale and Multi-feature Label Fusion

Automatic segmentation methods are important tools for quantitative anal...

0 Remi Giraud, et al. ∙

research

∙ 03/17/2019

Evaluation Framework of Superpixel Methods with a Global Regularity Measure

In the superpixel literature, the comparison of state-of-the-art methods...

0 Remi Giraud, et al. ∙

research

∙ 03/17/2019

SCALP: Superpixels with Contour Adherence using Linear Path

Superpixel decomposition methods are generally used as a pre-processing ...

0 Remi Giraud, et al. ∙

research

∙ 03/17/2019

Robust Shape Regularity Criteria for Superpixel Evaluation

Regular decompositions are necessary for most superpixel-based object re...

0 Remi Giraud, et al. ∙

research

∙ 03/14/2019

Superpixel-based Color Transfer

In this work, we propose a fast superpixel-based color transfer method (...

0 Remi Giraud, et al. ∙

research

∙ 01/30/2019

Texture-Aware Superpixel Segmentation

Most superpixel methods are based on spatial and color measures at the p...

0 Remi Giraud, et al. ∙

research

∙ 01/27/2019

Approximation of Wasserstein distance with Transshipment

An algorithm for approximating the p-Wasserstein distance between histog...

0 Nicolas Papadakis, et al. ∙

research

∙ 04/24/2018

Data-driven regularization of Wasserstein barycenters with an application to multivariate density registration

We present a framework to simultaneously align and smooth data in the fo...

0 Jérémie Bigot, et al. ∙

research

∙ 11/24/2017

Central limit theorems for Sinkhorn divergence between probability distributions on finite spaces and statistical applications

The notion of Sinkhorn divergence has recently gained popularity in mach...

0 Jérémie Bigot, et al. ∙

research

∙ 11/12/2017

Parameter Estimation in Finite Mixture Models by Regularized Optimal Transport: A Unified Framework for Hard and Soft Clustering

In this short paper, we formulate parameter estimation for finite mixtur...

0 Arnaud Dessein, et al. ∙

research

∙ 10/20/2016

Regularized Optimal Transport and the Rot Mover's Distance

This paper presents a unified framework for smooth convex regularization...

0 Arnaud Dessein, et al. ∙

research

∙ 10/05/2016

Convex Histogram-Based Joint Image Segmentation with Regularized Optimal Transport Cost

We investigate in this work a versatile convex framework for multiple im...

0 Nicolas Papadakis, et al. ∙

research

∙ 03/06/2015

Convex Color Image Segmentation with Optimal Transport Distances

This work is about the use of regularized optimal-transport distances fo...

0 Julien Rabin, et al. ∙

research

∙ 07/21/2013

Regularized Discrete Optimal Transport

This article introduces a generalization of the discrete optimal transpo...

0 Sira Ferradans, et al. ∙

Nicolas Papadakis

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