Publications related to Segmentation-driven 6D Object Pose Estimation

Adaptive hot water production based on Supervised Learning

A major challenge in the common approach of hot water generation in residential houses lies in the highly stochastic nature of domestic hot water (DHW) demand. Learning hot water use behavior enables water heating systems to continuously adapt to the stoch ...

2020

Single-Stage 6D Object Pose Estimation

Pascal Fua, Mathieu Salzmann, Wei Wang, Yinlin Hu

Most recent 6D pose estimation frameworks first rely on a deep network to establish correspondences between 3D object keypoints and 2D image locations and then use a variant of a RANSAC-based Perspective-n-Point (PnP) algorithm. This two-stage process, how ...

2020

Bayesian modeling of brain function and behavior

Maya Anna Jastrzebowska

The application of Bayesian modeling techniques is increasingly common in neuroscience due to the coherent and principled way in which the paradigm deals with uncertainty. The Bayesian framework is particularly valuable in the context of complex, ill-posed ...

EPFL2020

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

Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

Qian Wang, Jan Sickmann Hesthaven, Nicolò Ripamonti

Closure modeling based on the Mori-Zwanzig formalism has proven effective to improve the stability and accuracy of projection-based model order reduction. However, closure models are often expensive and infeasible for complex nonlinear systems. Towards eff ...

2019

On Certifying Non-Uniform Bounds against Adversarial Attacks

Volkan Cevher, Chen Liu

This work studies the robustness certification problem of neural network models, which aims to find certified adversary-free regions as large as possible around data points. In contrast to the existing approaches that seek regions bounded uniformly along a ...

2019

Geometry of adversarial robustness of deep networks: methods and applications

Seyed Mohsen Moosavi Dezfooli

We are witnessing a rise in the popularity of using artificial neural networks in many fields of science and technology. Deep neural networks in particular have shown impressive classification performance on a number of challenging benchmarks, generally in ...

EPFL2019

Context-Aware Crowd Counting

Pascal Fua, Mathieu Salzmann, Weizhe Liu

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. They typically use the same filters over the whole image or over large image patches. Only then do they estimate local scale to compensate for p ...

IEEE/CVF2019

Exploring Factors for Improving Low Resolution Face Recognition

Jean-Philippe Thiran, Hazim Kemal Ekenel

State-of-the-art deep face recognition approaches report near perfect performance on popular benchmarks, e.g., Labeled Faces in the Wild. However, their performance deteriorates significantly when they are applied on low quality images, such as those acqui ...

IEEE2019

Segmentation-driven 6D Object Pose Estimation

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Adaptive hot water production based on Supervised Learning

Single-Stage 6D Object Pose Estimation

Bayesian modeling of brain function and behavior

Shape Reconstruction by Learning Differentiable Surface Representations

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

Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

On Certifying Non-Uniform Bounds against Adversarial Attacks

Geometry of adversarial robustness of deep networks: methods and applications

Context-Aware Crowd Counting

Exploring Factors for Improving Low Resolution Face Recognition

Adaptive hot water production based on Supervised Learning

Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

Geometry of adversarial robustness of deep networks: methods and applications

Single-Stage 6D Object Pose Estimation

Context-Aware Crowd Counting

Shape Reconstruction by Learning Differentiable Surface Representations

On Certifying Non-Uniform Bounds against Adversarial Attacks

Bayesian modeling of brain function and behavior

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

Exploring Factors for Improving Low Resolution Face Recognition