Pierre Vandergheynst

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Unions of graph Fourier multipliers are an important class of linear operators for processing signals defined on graphs. We present a novel method to efficiently distribute the application of these operators to the high-dimensional signals collected by sen ...

In this work we present a method to perform a complete audiovisual source separation without need of previous information. This method is based on the assumption that sounds are caused by moving structures. Thus, an efficient representation of audio and vi ...

In this paper we consider the problem of sampling far below the Nyquist rate signals that are sparse linear superpositions of shifts of a known, potentially wide-band, pulse. This signal model is key for applications such as Ultra Wide Band (UWB) communica ...

Detecting and tracking people in scenes monitored by cameras is an important step in many application scenarios such as surveillance, urban planning or behavioral studies to name a few. The amount of data produced by camera feeds is so large that it is als ...

The performances of approximation using redundant expansions rely on having dictionaries adapted to the signals. In natural high-dimensional data, the statistical dependencies are, most of the time, not obvious. Learning fundamental patterns is an alternat ...

We present a novel approach to the reconstruction of depth from light field data. Our method uses dictionary representations and group sparsity constraints to derive a convex formulation. Although our solution results in an increase of the problem dimensio ...

A new sampling theorem on the sphere has been developed recently, reducing the number of samples required to represent a band-limited signal by a factor of two for equiangular sampling schemes. For signals sparse in a spatially localised measure, such as i ...

This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that ...

This paper describes a novel framework for compressive sampling of multichannel signals that are highly correlated across the channels. In this work, we assume few number of independent sources are generating the multichannel observations based on a linear ...

In this paper, we propose an energy functional to segment objects whose global shape is

We propose to evaluate our sparsity driven people localization framework on crowded complex scenes. The problem is recast as a linear inverse problem. It relies on deducing an occupancy vector, i.e. the discretized occupancy of people on the ground, from t ...

A method for automatic localization of objects in a mask. The method includes building a dictionary or atoms, wherein each atom models the presence of one object at one location and iteratively determining the atom of said dictionary which is best correlat ...

Computational representation of molecules can take many forms, including graphs, string encodings of graphs, binary vectors or learned embeddings in the form of real-valued vectors. These representations are then used in downstream classification and regre ...

In this work, we propose an automatic evaluation and comparison of the browsing behavior of Wikipedia readers that can be applied to any language editions of Wikipedia. As an example, we focus on English, French, and Russian languages during the last four ...

Mobile phone data provides rich dynamic information on human activities in social network analysis. In this paper, we represent data from two different modalities as a graph and functions defined on the vertex set of the graph. We propose a regularization ...

Matching Pursuit is a greedy algorithmthat decomposes any signal into a linear expansion of waveforms taken from a redundant dictionary. Computing the projection of the signal on every element of the basis has a high computational cost. To reduce this computational cost, optimized computational error minimization methods have to be found. Genetic Algorithms have shown to be a good tool to this approach.

This paper introduces

p

-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latte ...

We propose a method for signal reconstruction in semi-parametric dictionaries. The proposed algorithm estimates both the signal decomposition and the intrinsic parameters of the dictionary during the reconstruction process. Theoretical results about the co ...

Under resource constraints, LLMs are usually fine-tuned with additional knowledge using Parameter Efficient Fine-Tuning (PEFT), using Low-Rank Adaptation (LoRA) modules. In fact, LoRA injects a new set of small trainable matrices to adapt an LLM to a new t ...

This paper presents an algorithm to correlate audio and visual data generated by the same physical phenomenon. According to psychophysical experiments, temporal synchrony strongly contributes to integrate cross-modal information in humans. Thus, we define ...

Pierre Vandergheynst

Chattez avec Graph Search

Chebyshev Polynomial Approximation for Distributed Signal Processing

Blind Audiovisual Separation based on Redundant Representations

Compressive Sampling of Pulse Trains : Spread the Spectrum !

SCOOP: A Real-Time Sparsity Driven People Localization Algorithm

MoTIF: An Efficient Algorithm for Learning Translation Invariant Dictionaries

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Sparse image reconstruction on the sphere: implications of a new sampling theorem

Learning sparse generative models of audiovisual signals

Multichannel Compressed Sensing via Source Separation for Hyperspectral Images

A Priori Information in Image Segmentation: Energy Functional based on Shape Statistical Model and Image Information

Sparsity-driven People Localization Algorithm: Evaluation in Crowded Scenes Environments

Method and system for automatic objects localization

Molecular set representation learning

What is Trending on Wikipedia? Capturing Trends and Language Biases Across Wikipedia Editions

A Regularization Framework for Mobile Social Network Analysis

Matching Pursuit through Genetic Algorithms

Average case analysis of multichannel sparse approximations using p- thresholding

Robust joint reconstruction of misaligned images using semi-parametric dictionaries

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Audiovisual Gestalts