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Mathieu Salzmann

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Publications associées (169)

Unsupervised Geometry-Aware Representation Learning for 3D Human Pose Estimation

Pascal Fua, Mathieu Salzmann, Helge Jochen Rhodin

Modern 3D human pose estimation techniques rely on deep networks, which require large amounts of training data. While weakly-supervised methods require less supervision, by utilizing 2D poses or multi-view imagery without annotations, they still need a suf ...

2018

Statistically-Motivated Second-order Pooling

Mathieu Salzmann, Kaicheng Yu

Second-order pooling, a.k.a. bilinear pooling, has proven effective for deep learning based visual recognition. However, the resulting second-order networks yield a final representation that is orders of magnitude larger than that of standard, first-order ...

2018

A Domain-Adaptive Two-Stream U-Net For Electron Microscopy Image Segmentation

Pascal Fua, Mathieu Salzmann, Pablo Marquez Neila

Deep networks such as the U-Net are outstanding at segmenting biomedical images when enough training data is available, but only then. Here we introduce a Domain Adaptation approach that relies on two coupled U-Nets that either regularize or share correspo ...

IEEE2018

Learning Monocular 3D Human Pose Estimation from Multi-view Images

Pascal Fua, Mathieu Salzmann, Frédéric Meyer, Victor Constantin, Isinsu Katircioglu, Helge Jochen Rhodin

Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets. However, this still leaves open the problem of capturing motions for which no such database exists. Ma ...

IEEE2018

3D Box Proposals from a Single Monocular Image of an Indoor Scene

Mathieu Salzmann, Wei Zhuo

Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bounding boxes. Nevertheless, existing 3D box proposal techniques all assume h ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2018

Geometry-aware Deep Network for Single-Image Novel View Synthesis

Mathieu Salzmann

This paper tackles the problem of novel view synthesis from a single image. In particular, we target real-world scenes with rich geometric structure, a challenging task due to the large appearance variations of such scenes and the lack of simple 3D models ...

IEEE2018

Residual Parameter Transfer for Deep Domain Adaptation

Pascal Fua, Mathieu Salzmann, Artem Rozantsev

The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or none. Most current approaches have focused on learning feature representation ...

2018

A Domain-Adaptive Two-Stream U-Net for Electron Microscopy Image Segmentation

Pascal Fua, Mathieu Salzmann, Pablo Marquez Neila, Róger Bermúdez Chacón

2018

Learning to Reconstruct Texture-less Deformable Surfaces from a Single View

Pascal Fua, Mathieu Salzmann, Jan Bednarík

Recent years have seen the development of mature solutions for reconstructing deformable surfaces from a single image, provided that they are relatively well-textured. By contrast, recovering the 3D shape of texture-less surfaces remains an open problem, a ...

IEEE2018

Learning to Segment 3D Linear Structures Using Only 2D Annotations

Pascal Fua, Mathieu Salzmann, Agata Justyna Mosinska, Mateusz Kozinski

We propose a loss function for training a Deep Neural Network (DNN) to segment volumetric data, that accommodates ground truth annotations of 2D projections of the training volumes, instead of annotations of the 3D volumes themselves. In consequence, we si ...

SPRINGER INTERNATIONAL PUBLISHING AG2018

Indoor Scene Parsing with Instance Segmentation, Semantic Labeling and Support Relationship Inference

Mathieu Salzmann, Wei Zhuo

Over the years, indoor scene parsing has attracted a growing interest in the computer vision community. Existing methods have typically focused on diverse subtasks of this challenging problem. In particular, while some of them aim at segmenting the image i ...

Ieee2017

Boundary-aware Instance Segmentation

Mathieu Salzmann

We address the problem of instance-level semantic segmentation, which aims at jointly detecting, segmenting and classifying every individual object in an image. In this context, existing methods typically propose candidate objects, usually as bounding boxe ...

Ieee2017

Deep Subspace Clustering Networks

Mathieu Salzmann, Hongwei Li, Tong Zhang

2017

Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation

Pascal Fua, Mathieu Salzmann, Bugra Tekin, Pablo Marquez Neila

2017

Efficient Linear Programming for Dense CRFs

Mathieu Salzmann

The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a dense CRF can be minimized accurately using a linear programming (LP) relaxat ...

2017

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Mathieu Salzmann

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Unsupervised Geometry-Aware Representation Learning for 3D Human Pose Estimation

Statistically-Motivated Second-order Pooling

A Domain-Adaptive Two-Stream U-Net For Electron Microscopy Image Segmentation

Learning Monocular 3D Human Pose Estimation from Multi-view Images

3D Box Proposals from a Single Monocular Image of an Indoor Scene

Geometry-aware Deep Network for Single-Image Novel View Synthesis

Residual Parameter Transfer for Deep Domain Adaptation

A Domain-Adaptive Two-Stream U-Net for Electron Microscopy Image Segmentation

Learning to Reconstruct Texture-less Deformable Surfaces from a Single View

Learning to Segment 3D Linear Structures Using Only 2D Annotations

Indoor Scene Parsing with Instance Segmentation, Semantic Labeling and Support Relationship Inference

Boundary-aware Instance Segmentation

Deep Subspace Clustering Networks

Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation

Efficient Linear Programming for Dense CRFs

Joint Dimensionality Reduction and Metric Learning: A Geometric Take

Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation

Compression-aware Training of Deep Networks

Efficient Model-free Anthropometry from Depth Data

Encouraging LSTMs to Anticipate Actions Very Early