Publications related to BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration

Shape Reconstruction by Learning Differentiable Surface Representations

Pascal Fua, Mathieu Salzmann, Shaifali Parashar, Jan Bednarík, Erhan Gündogdu

Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations. Unfortunately, they offer no control over the deformations of the su ...

2020

Square Attack: a query-efficient black-box adversarial attack via random search

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

We propose the Square Attack, a new score-based black-box

l_2

and

l_\infty

adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. The Square Attack is based on a randomized search scheme where ...

2020

Neural Scene Decomposition for Multi-Person Motion Capture

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

Learning general image representations has proven key to the success of many computer vision tasks. For example, many approaches to image understanding problems rely on deep networks that were initially trained on ImageNet, mostly because the learned featu ...

IEEE2019

Biologically plausible deep learning – but how far can we go with shallow networks?

Wulfram Gerstner, Johanni Michael Brea, Bernd Albert Illing

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining s ...

2019

Detecting the Unexpected via Image Resynthesis

Pascal Fua, Mathieu Salzmann, Krzysztof Maciej Lis, Krishna Kanth Nakka

Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes ...

IEEE2019

Recurrent U-Net for Resource-Constrained Segmentation

Pascal Fua, Mathieu Salzmann, Joachim Ludovic Hugonot, Wei Wang, Kaicheng Yu

State-of-the-art segmentation methods rely on very deep networks that are not always easy to train without very large training datasets and tend to be relatively slow to run on standard GPUs. In this paper, we introduce a novel recurrent U-Net architecture ...

IEEE COMPUTER SOC2019

Federated Generative Privacy

Boi Faltings, Aleksei Triastcyn

In this paper, we propose FedGP, a framework for privacy-preserving data release in the federated learning setting. We use generative adversarial networks, generator components of which are trained by FedAvg algorithm, to draw privacy-preserving artificial ...

2019

Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation

Pascal Fua, Mathieu Salzmann, Weizhe Liu, Krzysztof Maciej Lis

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density in the image plane. While useful for this purpose, this image- plane density has no immediate physical meaning because it is subject to perspecti ...

IEEE2019

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Bin Jin

Imaging devices have become ubiquitous in modern life, and many of us capture an increasing number of images every day. When we choose to share or store some of these images, our primary selection criterion is to choose the most visually pleasing ones. Yet ...

EPFL2018

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Bugra Tekin

Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growin ...

EPFL2018

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration

Graph Chatbot

Chat with Graph Search

Shape Reconstruction by Learning Differentiable Surface Representations

Square Attack: a query-efficient black-box adversarial attack via random search

Neural Scene Decomposition for Multi-Person Motion Capture

Biologically plausible deep learning – but how far can we go with shallow networks?

Detecting the Unexpected via Image Resynthesis

Recurrent U-Net for Resource-Constrained Segmentation

Federated Generative Privacy

Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Federated Generative Privacy

Biologically plausible deep learning – but how far can we go with shallow networks?

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Square Attack: a query-efficient black-box adversarial attack via random search

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Neural Scene Decomposition for Multi-Person Motion Capture

Shape Reconstruction by Learning Differentiable Surface Representations

Recurrent U-Net for Resource-Constrained Segmentation

Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation

Detecting the Unexpected via Image Resynthesis