1 code implementation • Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2023 • Hernan Carrillo, Michaël Clément, Aurélie Bugeau, Edgar Simo-Serra
Colorization of line art drawings is an important task in illustration and animation workflows.
no code implementations • 14 Jun 2023 • Lucia Bouza Heguerte, Aurélie Bugeau, Loïc Lannelongue
Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society.
no code implementations • 14 Sep 2022 • Warren Jouanneau, Aurélie Bugeau, Marc Palyart, Nicolas Papadakis, Laurent Vézard
In this paper, we propose a patch-based architecture for multi-label classification problems where only a single positive label is observed in images of the dataset.
no code implementations • 6 Apr 2022 • Coloma Ballester, Aurélie Bugeau, Hernan Carrillo, Michaël Clément, Rémi Giraud, Lara Raad, Patricia Vitoria
In this chapter, we aim to study their influence on the results obtained by training a deep neural network, to answer the question: "Is it crucial to correctly choose the right color space in deep-learning based colorization?".
no code implementations • 6 Apr 2022 • Coloma Ballester, Aurélie Bugeau, Hernan Carrillo, Michaël Clément, Rémi Giraud, Lara Raad, Patricia Vitoria
While learning to automatically colorize an image, one can define well-suited objective functions related to the desired color output.
no code implementations • 22 Oct 2021 • Anne-Laure Ligozat, Julien Lefèvre, Aurélie Bugeau, Jacques Combaz
In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and in the first place greenhouse gas emissions (GHG).
no code implementations • 8 Oct 2020 • Giorgia Pitteri, Aurélie Bugeau, Slobodan Ilic, Vincent Lepetit
We demonstrate the performance of this approach on the T-LESS dataset, by using a small number of objects to learn the embedding and testing it on the other objects.
no code implementations • 26 May 2020 • Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Mark Kirkland, Peter Schuetz, Carola-Bibiane Schönlieb
The subproblems are handled by a framework that consists of 1) a segmentation task that learns from pixel-level ground truth segmentation masks of a small fraction of the images, 2) a recursive approximation task that conducts partial object regions learning and data-driven mask evolution starting from partial masks of each object instance, and 3) other problem oriented auxiliary tasks that are trained with sparse annotations and promote the learning of dedicated features.
no code implementations • 4 Oct 2019 • Simone Parisotto, Luca Calatroni, Aurélie Bugeau, Nicolas Papadakis, Carola-Bibiane Schönlieb
We propose a new variational model for non-linear image fusion.
no code implementations • 25 Sep 2019 • Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Peter Schuetz, Carola-Bibiane Schönlieb
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for image segmentation.
1 code implementation • 30 Aug 2019 • Pierre Biasutti, Vincent Lepetit, Jean-François Aujol, Mathieu Brédif, Aurélie Bugeau
We propose LU-Net -- for LiDAR U-Net, a new method for the semantic segmentation of a 3D LiDAR point cloud.
no code implementations • 20 Jun 2019 • Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Peter Schuetz, Carola-Bibiane Schönlieb
In this paper, we introduce a deep convolutional neural network for microscopy image segmentation.
no code implementations • 21 May 2019 • Pierre Biasutti, Aurélie Bugeau, Jean-François Aujol, Mathieu Brédif
This paper proposes RIU-Net (for Range-Image U-Net), the adaptation of a popular semantic segmentation network for the semantic segmentation of a 3D LiDAR point cloud.
no code implementations • 17 Mar 2019 • Rémi Giraud, Vinh-Thong Ta, Aurélie Bugeau, Pierrick Coupé, Nicolas Papadakis
Superpixels have become very popular in many computer vision applications.