Home

Person

Mathieu Salzmann

About
Privacy
Disclaimer

Graph Chatbot

Related publications (169)

Imposing Hard Constraints on Deep Networks: Promises and Limitations

Pascal Fua, Mathieu Salzmann, Pablo Marquez Neila

2017

Distribution-Matching Embedding for Visual Domain Adaptation

Mathieu Salzmann

Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typical ...

2016

Efficient Transductive Semantic Segmentation

Mathieu Salzmann

Semantically describing the contents of images is one of the classical problems of computer vision. With huge numbers of images being made available daily, there is increasing interest in methods for semantic pixel labelling that exploit large image sets. ...

Ieee2016

Scalable Unsupervised Domain Adaptation for Electron Microscopy

Pascal Fua, Mathieu Salzmann, Carlos Joaquin Becker, Róger Bermúdez Chacón

While Machine Learning algorithms are key to automating organelle segmentation in large EM stacks, they require annotated data, which is hard to come by in sufficient quantities. Furthermore, images acquired from one part of the brain are not always repres ...

2016

Exploiting Large Image Sets for Road Scene Parsing

Mathieu Salzmann

There is an increasing interest in exploiting multiple images for scene understanding, with great progress in areas such as cosegmentation and video segmentation. Jointly analyzing the images in a large set offers the opportunity to exploit a greater sourc ...

IEEE Institute of Electrical and Electronics Engineers2016

Segmentation of Developing Human Embryo in Time-lapse Microscopy

Mathieu Salzmann

Being able to efficiently segment a developing embryo from background clutter constitutes an important step in automated monitoring of human embryonic cells. State-of-the-art automatic segmentation methods remain ill-suited to handle the complex behavior a ...

IEEE2016

Building Scene Models by Completing and Hallucinating Depth and Semantics

Mathieu Salzmann

Building 3D scene models has been a longstanding goal of computer vision. The great progress in depth sensors brings us one step closer to achieving this in a single shot. However, depth sensors still produce imperfect measurements that are sparse and cont ...

Springer Int Publishing Ag2016

Learning the Number of Neurons in Deep Networks

Mathieu Salzmann

2016

Template-free 3D Reconstruction of Poorly-textured Nonrigid Surfaces

Mathieu Salzmann, Fei Wang, Xinchao Wang

Two main classes of approaches have been studied to perform monocular nonrigid 3D reconstruction: Template-based methods and Non-rigid Structure from Motion techniques. While the first ones have been applied to reconstruct poorly-textured surfaces, they as ...

Springer Int Publishing Ag2016

When VLAD met Hilbert

Mathieu Salzmann

2016

Robust Multi-body Feature Tracker: A Segmentation-free Approach

Mathieu Salzmann, Hongwei Li

2016

Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering

Mathieu Salzmann, Mohammad Najafi

2016

Memory Efficient Max Flow for Multi-label Submodular MRFs

Mathieu Salzmann

2016

Learning to Co-Generate Object Proposals with a Deep Structured Network

Mathieu Salzmann

2016

Structured Prediction of 3D Human Pose with Deep Neural Networks

Pascal Fua, Mathieu Salzmann, Vincent Lepetit, Bugra Tekin, Isinsu Katircioglu

Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from image to 3D pose, which ignores the dependencies between human joints, or model these dependencies via a ...

2016

Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

Mathieu Salzmann, Fatemehsadat Saleh

Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using im ...

Springer Int Publishing Ag2016

Monocular 3D Reconstruction of Locally Textured Surfaces

Pascal Fua, Mathieu Salzmann, Aydin Varol, Appu Shaji

Most recent approaches to monocular non-rigid 3D shape recovery rely on exploiting point correspondences and work best when the whole surface is well-textured. The alternative is to rely either on contours or shading information, which has only been demons ...

2012

Linear Local Models for Monocular Reconstruction of Deformable Surfaces

Pascal Fua, Mathieu Salzmann

Recovering the 3D shape of a nonrigid surface from a single viewpoint is known to be both ambiguous and challenging. Resolving the ambiguities typically requires prior knowledge about the most likely deformations that the surface may undergo. It often take ...

2011

Learning and recovering 3D surface deformations

Mathieu Salzmann

Recovering the 3D deformations of a non-rigid surface from a single viewpoint has applications in many domains such as sports, entertainment, and medical imaging. Unfortunately, without any knowledge of the possible deformations that the object of interest ...

EPFL2009

Template-Free Monocular Reconstruction of Deformable Surfaces

Pascal Fua, Mathieu Salzmann, Engin Tola, Aydin Varol

It has recently been shown that deformable 3D surfaces could be recovered from single video streams. However, existing techniques either require a reference view in which the shape of the surface is known a priori, which often may not be available, or requ ...

2009

Page 8 of 9