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Pascal Frossard

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Publications associées (428)

Graph-based Transform Coding with Application to Image Compression

Pascal Frossard, Ntorina Thanou, Giulia Fracastoro

In this paper, we propose a new graph-based coding framework and illustrate its application to image compression. Our approach relies on the careful design of a graph that optimizes the overall rate-distortion performance through an effective graph-based t ...

2019

Spherical Clustering of Users Navigating 360° Content

Pascal Frossard, Francesca De Simone, Laura Toni

In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on th ...

IEEE2019

Robustness via curvature regularization, and vice versa

Pascal Frossard, Alhussein Fawzi, Seyed Mohsen Moosavi Dezfooli

State-of-the-art classifiers have been shown to be largely vulnerable to adversarial perturbations. One of the most effective strategies to improve robustness is adversarial training. In this paper, we investigate the effect of adversarial training on the ...

IEEE2019

Graph Signal Representation with Wasserstein Barycenters

Pascal Frossard, Effrosyni Simou

IEEE2019

Graph-Based Detection of Seams In 360-Degree Images

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

In this paper, we propose an algorithm to detect a specific kind of distortions, referred to as seams, which commonly occur when a 360-degree image is represented in planar domain by projecting the sphere to a polyhedron, e.g, via the Cube Map (CM) project ...

2019

Graph-Based Non-Uniform Sampling and Reconstruction of Depth Maps

Pascal Frossard, Jingyu Yang, Kun Li, Xinchen Ye

IEEE2019

GOT: An Optimal Transport framework for Graph comparison

Pascal Frossard, Giovanni Chierchia, Hermina Petric Maretic, Mireille El Gheche

We present a novel framework based on optimal transport for the challenging problem of comparing graphs. Specifically, we exploit the probabilistic distribution of smooth graph signals defined with respect to the graph topology. This allows us to derive an ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Learning anisotropic filters on product graphs

Pascal Frossard, Clément Arthur Yvon Vignac

The extension of convolutional neural networks to irregular domains has pavedthe way to promising graph data analysis methods. It has however come at theexpense of a reduced representation power, as most of these new network archi-tectures can only learn i ...

2019

Universal Adversarial Attacks on Text Classifiers

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Melika Behjati

Despite the vast success neural networks have achieved in different application domains, they have been proven to be vulnerable to adversarial perturbations (small changes in the input), which lead them to produce the wrong output. In this paper, we propos ...

IEEE2019

Automatic Segmentation of Nuclei in Histopathology Images using Encoding-Decoding Convolutional Neural Networks

Pascal Frossard, Beril Besbinar, Deniz Mercadier

Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, ...

IEEE2019

Global Auto-regressive Depth Recovery via Iterative Non-local Filtering

Pascal Frossard, Jingyu Yang, Xinchen Ye

Existing depth sensing techniques have many shortcomings in terms of resolution, completeness, and accuracy. The performance of 3-D broadcasting systems is therefore limited by the challenges of capturing high-resolution depth data. In this paper, we prese ...

IEEE2019

Learning Graphs From Data: A Signal Representation Perspective

Pascal Frossard, Ntorina Thanou, Xiaowen Dong

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis, and visualization of structured data. When a natural choice of the graph is not readily available from the data sets, it is thus des ...

2019

Visual Distortions in 360-degree videos (Dataset)

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

Here we catalog, visually demonstrate, and discuss the main causes of commonly encountered visual distortions into current 360-degree video processing pipelines. The main goal of the samples in this dataset is to demonstrate the visual distortions discusse ...

2019

A Geometry-Inspired Decision-Based Attack

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Yujia Liu

Deep neural networks have recently achieved tremendous success in image classification. Recent studies have however shown that they are easily misled into incorrect classification decisions by adversarial examples. Adversaries can even craft attacks by que ...

IEEE COMPUTER SOC2019

Adaptive Streaming in Interactive Multiview Video Systems

Pascal Frossard, Laura Toni, Xue Zhang, Yao Zhao

Multiview applications endow final users with the possibility to freely navigate within 3D scenes with minimum-delay. A real feeling of scene navigation is enabled by transmitting multiple high-quality camera views, which can be used to synthesize addition ...

2019

Subjective Evaluation of 360-degree Sensory Experiences

Pascal Frossard, Roberto Gerson De Albuquerque Azevedo

Traditionally, most multimedia content has been developed to stimulate two of the human senses, i.e., sight and hearing. Due to recent technological advancements, however, innovative services have been developed that provide more realistic, immersive, and ...

IEEE2019

Online Resource Inference in Network Utility Maximization Problems

Pascal Frossard, Stefano D'Aronco

The amount of transmitted data in computer networks is expected to grow considerably in the future, putting more and more pressure on the network infrastructures. In order to guarantee a good service, it then becomes fundamental to use the network resource ...

2019

Analog Neural Networks with Deep-submicron Nonlinear Synapses

Pascal Frossard, Babak Falsafi, Ahmet Caner Yüzügüler, Mario Paulo Drumond Lages De Oliveira, Firat Çelik

Deep neural network (DNN) inference tasks are computationally expensive. Digital DNN accelerators offer better density and energy efficiency than general-purpose processors but still not sufficient to be deployable on resource-constrained settings.Analog c ...

2019

Optimized Quantization in Distributed Graph Signal Filtering

Pascal Frossard, Isabela Cunha Maia Nobre

Distributed graph signal processing algorithms require the network nodes to communicate by exchanging messages in order to achieve a common objective. These messages have a finite precision in realistic networks, which may necessitate to implement message ...

2019

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Pascal Frossard, Arun Venkitaraman

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse ...

IEEE2019

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