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Pascal Frossard

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Publications associées (428)

Neural Anisotropy Directions

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Apostolos Modas, Guillermo Ortiz Jimenez

In this work, we analyze the role of the network architecture in shaping the inductive bias of deep classifiers. To that end, we start by focusing on a very simple problem, i.e., classifying a class of linearly separable distributions, and show that, depen ...

2020

Graph Laplacian Mixture Model

Pascal Frossard, Hermina Petric Maretic

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all avai ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

GeoDA: a geometric framework for black-box adversarial attacks

Pascal Frossard

Adversarial examples are known as carefully perturbed images fooling image classifiers. We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number ...

IEEE2020

Optimism in the Face of Adversity: Understanding and Improving Deep Learning Through Adversarial Robustness

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Apostolos Modas, Guillermo Ortiz Jimenez

Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications. Nevertheless, our current theoretical understanding on the mathematical fo ...

2020

Benchmark for Human-to-Robot Handovers of Unseen Containers with Unknown Filling

Pascal Frossard, Aude Billard, Andrea Cavallaro, Konstantinos Chatzilygeroudis, Nuno Ricardo Ferreira Duarte, Apostolos Modas

The real-time estimation through vision of the physical properties of objects manipulated by humans is important to inform the control of robots for performing accurate and safe grasps of objects handed over by humans. However, estimating the 3D pose and d ...

2020

Joint Graph-based Depth Refinement and Normal Estimation

Pascal Frossard, Mattia Rossi, Mireille El Gheche

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenes are characterized by a prevalence of human made structures, which in most of the cases, are either ...

2020

Toward Robust Sensing for Autonomous Vehicles: An Adversarial Perspective

Pascal Frossard, Andrea Cavallaro, Apostolos Modas

Autonomous vehicles (AVs) rely on accurate and robust sensor observations for safety-critical decision making in a variety of conditions. The fundamental building blocks of such systems are sensors and classifiers that process ultrasound, radar, GPS, lidar ...

2020

Multi-View Shape Estimation of Transparent Containers

Pascal Frossard, Andrea Cavallaro, Apostolos Modas

The 3D localisation of an object and the estimation of its properties, such as shape and dimensions, are challenging under varying degrees of transparency and lighting conditions. In this paper, we propose a method for jointly localising container-like obj ...

IEEE2020

Subjective and Viewport-based Objective Quality Assessment of 360-degree Videos

Pascal Frossard, Roberto Gerson De Albuquerque Azevedo

Visual distortions in processed 360-degree visual content and consumed through head-mounted displays (HMDs) are perceived very differently when compared to traditional 2D content. To better understand how compression-related artifacts affect the overall pe ...

2020

Mask Combination of Multi-Layer Graphs for Global Structure Inference

Pascal Frossard, Ntorina Thanou, Elif Vural, Eda Bayram

Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although ...

2020

Hold me tight! Influence of discriminative features on deep network boundaries

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Apostolos Modas, Guillermo Ortiz Jimenez

Important insights towards the explainability of neural networks reside in the characteristics of their decision boundaries. In this work, we borrow tools from the field of adversarial robustness, and propose a new perspective that relates dataset features ...

2020

A Viewport-driven Multi-metric Fusion Approach for 360-Degree Video Quality Assessment

Pascal Frossard, Roberto Gerson De Albuquerque Azevedo

We propose a new viewport-based multi-metric fusion (MMF) approach for visual quality assessment of 360-degree (omnidirectional) videos. Our method is based on computing multiple spatio-temporal objective quality metrics (features) on viewports extracted f ...

IEEE2020

On The Choice of Graph Neural Network Architectures

Pascal Frossard, Clément Arthur Yvon Vignac, Guillermo Ortiz Jimenez

Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the development of graph networks, this setup has become a de facto benchmark for a sign ...

IEEE2020

Modurec: Recommender Systems with Feature and Time Modulation

Pascal Frossard, Clément Arthur Yvon Vignac, Javier Alejandro Maroto Morales

Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effective use of other features, which c ...

IEEE2020

OrthoNet: Multilayer Network Data Clustering

Pascal Frossard, Giovanni Chierchia, Mireille El Gheche

Network data appears in very diverse applications, like biological, social, or sensor networks. Clustering of network nodes into categories or communities has thus become a very common task in machine learning and data mining. Network data comes with some ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Redundant features can hurt robustness to distribution shift

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Apostolos Modas, Guillermo Ortiz Jimenez

In this work, we borrow tools from the field of adversarial robustness, and propose a new framework that permits to relate dataset features to the distance of samples to the decision boundary. Using this framework we identify the subspace of features used ...

2020

Visual Distortions in 360-degree Videos

Pascal Frossard, Francesca De Simone, Roberto Gerson De Albuquerque Azevedo

Omnidirectional (or 360-degree) images and videos are emergent signals being used in many areas such as robotics and virtual/augmented reality. In particular, for virtual reality applications, they allow an immersive experience in which the user can intera ...

2020

Optimized Quantization in Distributed Graph Signal Processing

Pascal Frossard, Isabela Cunha Maia Nobre

Distributed graph signal processing methods require that the graph nodes communicate by exchanging messages. These messages have a finite precision in a realistic network, which may necessitate to implement quantization. Quantization, in turn, generates er ...

IEEE2019

On the first JND and break in presence of 360-degree content

Pascal Frossard, Roberto Gerson De Albuquerque Azevedo

Unlike traditional planar 2D visual content, immersive 360-degree images and videos undergo particular processing steps and are intended to be consumed via head-mounted displays (HMDs). To get a deeper understanding on the perception of 360-degree visual d ...

ACM Press2019

Une approche basée incertitude pour transporter un signal d’un graphe à un autre

Pascal Frossard, Hermina Petric Maretic, Mireille El Gheche, Bastien Cyril Pasdeloup

2019

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