1 code implementation • ECCV 2020 • Luca Cosmo, Giorgia Minello, Michael Bronstein, Luca Rossi, Andrea Torsello
We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for points on non-rigid three-dimensional shapes based on the average mixing kernel and continuous-time quantum walks.
1 code implementation • 25 Apr 2024 • Ruben Ciranni, Emilian Postolache, Giorgio Mariani, Michele Mancusi, Luca Cosmo, Emanuele Rodolà
We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a contrastive learning method for musical audio representations that captures the harmonic and rhythmic coherence between samples.
1 code implementation • 18 Mar 2024 • Emilian Postolache, Giorgio Mariani, Luca Cosmo, Emmanouil Benetos, Emanuele Rodolà
Multi-Source Diffusion Models (MSDM) allow for compositional musical generation tasks: generating a set of coherent sources, creating accompaniments, and performing source separation.
no code implementations • 29 Feb 2024 • Giorgia Minello, Alessandro Bicciato, Luca Rossi, Andrea Torsello, Luca Cosmo
In this paper, we present GRASP, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process.
1 code implementation • 17 Jan 2024 • Alessandro Bicciato, Luca Cosmo, Giorgia Minello, Luca Rossi, Andrea Torsello
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning.
1 code implementation • 4 Feb 2023 • Giorgio Mariani, Irene Tallini, Emilian Postolache, Michele Mancusi, Luca Cosmo, Emanuele Rodolà
In this work, we define a diffusion-based generative model capable of both music synthesis and source separation by learning the score of the joint probability density of sources sharing a context.
1 code implementation • 9 Jan 2023 • Emilian Postolache, Giorgio Mariani, Michele Mancusi, Andrea Santilli, Luca Cosmo, Emanuele Rodolà
Autoregressive models have achieved impressive results over a wide range of domains in terms of generation quality and downstream task performance.
no code implementations • 30 May 2022 • Marco Pegoraro, Riccardo Marin, Arianna Rampini, Simone Melzi, Luca Cosmo, Emanuele Rodolà
We demonstrate the benefits of incorporating spectral maps in graph learning pipelines, addressing scenarios where a node-to-node map is not well defined, or in the absence of exact isomorphism.
no code implementations • 1 Apr 2022 • Kamilia Mullakaeva, Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein
In this work, we propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications that exploits the graph representation of the input data samples and their latent relation.
no code implementations • CVPR 2022 • Mahdi Saleh, Shun-Cheng Wu, Luca Cosmo, Nassir Navab, Benjamin Busam, Federico Tombari
Shape matching has been a long-studied problem for the computer graphics and vision community.
no code implementations • 14 Dec 2021 • Luca Cosmo, Giorgia Minello, Michael Bronstein, Emanuele Rodolà, Luca Rossi, Andrea Torsello
The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter.
1 code implementation • 11 Oct 2021 • Michele Mancusi, Emilian Postolache, Giorgio Mariani, Marco Fumero, Andrea Santilli, Luca Cosmo, Emanuele Rodolà
State of the art audio source separation models rely on supervised data-driven approaches, which can be expensive in terms of labeling resources.
Ranked #1 on Music Source Separation on Slakh2100
1 code implementation • NeurIPS 2021 • Giovanni Trappolini, Luca Cosmo, Luca Moschella, Riccardo Marin, Simone Melzi, Emanuele Rodolà
In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds.
no code implementations • CVPR 2021 • Arianna Rampini, Franco Pestarini, Luca Cosmo, Simone Melzi, Emanuele Rodolà
Our attacks are universal, in that they transfer across different shapes, different representations (meshes and point clouds), and generalize to previously unseen data.
1 code implementation • 31 Mar 2021 • Luca Moschella, Simone Melzi, Luca Cosmo, Filippo Maggioli, Or Litany, Maks Ovsjanikov, Leonidas Guibas, Emanuele Rodolà
Spectral geometric methods have brought revolutionary changes to the field of geometry processing.
no code implementations • 2 Mar 2021 • Marco Fumero, Luca Cosmo, Simone Melzi, Emanuele Rodolà
We propose a novel approach to disentangle the generative factors of variation underlying a given set of observations.
1 code implementation • ECCV 2020 • Luca Cosmo, Antonio Norelli, Oshri Halimi, Ron Kimmel, Emanuele Rodolà
In this paper, we advocate the adoption of metric preservation as a powerful prior for learning latent representations of deformable 3D shapes.
no code implementations • 27 Mar 2020 • Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein
Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer-Aided Diagnosis (CADx) and disease prediction.
1 code implementation • 11 Feb 2020 • Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein
We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning).
1 code implementation • CVPR 2019 • Luca Cosmo, Mikhail Panine, Arianna Rampini, Maks Ovsjanikov, Michael M. Bronstein, Emanuele Rodolà
The question whether one can recover the shape of a geometric object from its Laplacian spectrum ('hear the shape of the drum') is a classical problem in spectral geometry with a broad range of implications and applications.
no code implementations • ICCV 2017 • Filippo Bergamasco, Luca Cosmo, Andrea Gasparetto, Andrea Albarelli, Andrea Torsello
At the core of many Computer Vision applications stands the need to define a mathematical model describing the imaging process.
1 code implementation • 17 Jun 2015 • Emanuele Rodolà, Luca Cosmo, Michael M. Bronstein, Andrea Torsello, Daniel Cremers
In this paper, we propose a method for computing partial functional correspondence between non-rigid shapes.
no code implementations • CVPR 2015 • Filippo Bergamasco, Andrea Albarelli, Luca Cosmo, Andrea Torsello, Emanuele Rodola, Daniel Cremers
This results in several drawbacks, ranging from the difficulties in feature detection, due to the reduced size of each microlens, to the need to adopt a model with a relatively small number of parameters.