1 code implementation • ECCV 2020 • Oshri Halimi, Ido Imanuel, Or Litany, Giovanni Trappolini, Emanuele Rodolà, Leonidas Guibas, Ron Kimmel
Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner.
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.
no code implementations • 19 Apr 2024 • Antonio Pio Ricciardi, Valentino Maiorca, Luca Moschella, Riccardo Marin, Emanuele Rodolà
We build upon the recent relative representations framework and adapt it for Visual RL.
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 • 8 Mar 2024 • Francesco Palandra, Andrea Sanchietti, Daniele Baieri, Emanuele Rodolà
We present GSEdit, a pipeline for text-guided 3D object editing based on Gaussian Splatting models.
1 code implementation • 13 Nov 2023 • Chin-Yun Yu, Emilian Postolache, Emanuele Rodolà, György Fazekas
In this paper, we examine this problem in the context of duet singing voices separation, and propose a method to enforce the coherency of singer identity by splitting the mixture into overlapping segments and performing posterior sampling in an auto-regressive manner, conditioning on the previous segment.
no code implementations • 11 Nov 2023 • Donato Crisostomi, Irene Cannistraci, Luca Moschella, Pietro Barbiero, Marco Ciccone, Pietro Liò, Emanuele Rodolà
Models trained on semantically related datasets and tasks exhibit comparable inter-sample relations within their latent spaces.
1 code implementation • NeurIPS 2023 • Valentino Maiorca, Luca Moschella, Antonio Norelli, Marco Fumero, Francesco Locatello, Emanuele Rodolà
While different neural models often exhibit latent spaces that are alike when exposed to semantically related data, this intrinsic similarity is not always immediately discernible.
no code implementations • 23 Oct 2023 • Marco Comunità, Riccardo F. Gramaccioni, Emilian Postolache, Emanuele Rodolà, Danilo Comminiello, Joshua D. Reiss
Sound design involves creatively selecting, recording, and editing sound effects for various media like cinema, video games, and virtual/augmented reality.
no code implementations • 2 Oct 2023 • Irene Cannistraci, Luca Moschella, Marco Fumero, Valentino Maiorca, Emanuele Rodolà
It has been observed that representations learned by distinct neural networks conceal structural similarities when the models are trained under similar inductive biases.
1 code implementation • 31 Jul 2023 • Andrea Santilli, Emanuele Rodolà
In recent years Large Language Models (LLMs) have increased the state of the art on several natural language processing tasks.
1 code implementation • 3 Jul 2023 • Marco Pegoraro, Sanketh Vedula, Aviv A. Rosenberg, Irene Tallini, Emanuele Rodolà, Alex M. Bronstein
Quantile regression (QR) is a statistical tool for distribution-free estimation of conditional quantiles of a target variable given explanatory features.
1 code implementation • 26 Jun 2023 • Andrea Bacciu, Giovanni Trappolini, Andrea Santilli, Emanuele Rodolà, Fabrizio Silvestri
This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM).
no code implementations • 28 May 2023 • Marco Pegoraro, Clémentine Dominé, Emanuele Rodolà, Petar Veličković, Andreea Deac
Antibody-antigen interactions play a crucial role in identifying and neutralizing harmful foreign molecules.
3 code implementations • 17 May 2023 • Andrea Santilli, Silvio Severino, Emilian Postolache, Valentino Maiorca, Michele Mancusi, Riccardo Marin, Emanuele Rodolà
We propose to reframe the standard greedy autoregressive decoding of MT with a parallel formulation leveraging Jacobi and Gauss-Seidel fixed-point iteration methods for fast inference.
1 code implementation • 2 May 2023 • Giovanni Trappolini, Andrea Santilli, Emanuele Rodolà, Alon Halevy, Fabrizio Silvestri
The rise in loosely-structured data available through text, images, and other modalities has called for new ways of querying them.
no code implementations • NeurIPS 2023 • Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello
Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings.
no code implementations • 17 Mar 2023 • Daniele Baieri, Stefano Esposito, Filippo Maggioli, Emanuele Rodolà
Representing 3D surfaces as level sets of continuous functions over $\mathbb{R}^3$ is the common denominator of neural implicit representations, which recently enabled remarkable progress in geometric deep learning and computer vision tasks.
1 code implementation • 1 Mar 2023 • Irene Cannistraci, Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Emanuele Rodolà
The use of relative representations for latent embeddings has shown potential in enabling latent space communication and zero-shot model stitching across a wide range of applications.
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.
1 code implementation • 9 Jan 2023 • Emanuele Frascaroli, Riccardo Benaglia, Matteo Boschini, Luca Moschella, Cosimo Fiorini, Emanuele Rodolà, Simone Calderara
While biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget catastrophically whenever they face a changing training data distribution.
no code implementations • 30 Sep 2022 • Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, Emanuele Rodolà
Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations.
1 code implementation • 20 Sep 2022 • Giovanni Trappolini, Valentino Maiorca, Silvio Severino, Emanuele Rodolà, Fabrizio Silvestri, Gabriele Tolomei
In this work, we focus on a specific, white-box attack to GNN-based link prediction models, where a malicious node aims to appear in the list of recommended nodes for a given target victim.
no code implementations • 22 Jun 2022 • Stefano Esposito, Daniele Baieri, Stefan Zellmann, André Hinkenjann, Emanuele Rodolà
NeRF-based techniques fit wide and deep multi-layer perceptrons (MLPs) to a continuous radiance field that can be rendered from any unseen viewpoint.
1 code implementation • 8 Jun 2022 • Donato Crisostomi, Simone Antonelli, Valentino Maiorca, Luca Moschella, Riccardo Marin, Emanuele Rodolà
In this work, we tackle the problem of few-shot graph classification, showing that equipping a simple distance metric learning baseline with a state-of-the-art graph embedder allows to obtain competitive results on the task. While the simplicity of the architecture is enough to outperform more complex ones, it also allows straightforward additions.
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 • 20 Mar 2022 • Niloofar Azizi, Horst Possegger, Emanuele Rodolà, Horst Bischof
In particular, this allows us to directly and explicitly encode the transformation between joints, resulting in a significantly more compact representation.
Ranked #58 on 3D Human Pose Estimation on Human3.6M
1 code implementation • 25 Jan 2022 • Antonio Norelli, Giorgio Mariani, Luca Moschella, Andrea Santilli, Giambattista Parascandolo, Simone Melzi, Emanuele Rodolà
We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences -- e. g. explanations written in hieroglyphic -- by autonomously learning to interpret them.
no code implementations • 13 Jan 2022 • Michele Mancusi, Nicola Zonca, Emanuele Rodolà, Silvia Zuffi
Moreover, one of the causes of biodiversity loss is sound pollution; in data obtained from regions with loud anthropic noise, it is hard to separate the artificial from the fish sound manually.
1 code implementation • 14 Dec 2021 • Riccardo Marin, Souhaib Attaiki, Simone Melzi, Emanuele Rodolà, Maks Ovsjanikov
In this study, we analyze, for the first time, properties that arise in data-driven learned embedding and their relation to the shape-matching task.
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 • 4 Aug 2021 • Marco Pegoraro, Simone Melzi, Umberto Castellani, Riccardo Marin, Emanuele Rodolà
In this work, we address this problem by defining a data-driven model upon a family of linear operators (variants of the mesh Laplacian), whose spectra capture global and local geometric properties of the shape at hand.
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 • 29 Apr 2021 • Debora Caldarola, Massimiliano Mancini, Fabio Galasso, Marco Ciccone, Emanuele Rodolà, Barbara Caputo
Clustering may reduce heterogeneity by identifying the domains, but it deprives each cluster model of the data and supervision of others.
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.
no code implementations • 26 Nov 2020 • Or Litany, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Daniel Cremers
We assume to be given a reference shape and its multiple parts undergoing a non-rigid deformation.
no code implementations • 19 Sep 2020 • Riccardo Marin, Simone Melzi, Emanuele Rodolà, Umberto Castellani
This augmentation provides an effective workaround for the resolution limitations imposed by the adopted morphable model.
no code implementations • 28 May 2020 • Danilo Avola, Luigi Cinque, Alessio Fagioli, Sebastiano Filetti, Giorgio Grani, Emanuele Rodolà
Computer-aided diagnosis (CAD) is becoming a prominent approach to assist clinicians spanning across multiple fields.
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.
1 code implementation • 14 Mar 2020 • Riccardo Marin, Arianna Rampini, Umberto Castellani, Emanuele Rodolà, Maks Ovsjanikov, Simone Melzi
We introduce the first learning-based method for recovering shapes from Laplacian spectra.
1 code implementation • 27 Jan 2020 • Oshri Halimi, Ido Imanuel, Or Litany, Giovanni Trappolini, Emanuele Rodolà, Leonidas Guibas, Ron Kimmel
Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner.
2 code implementations • 16 Apr 2019 • Simone Melzi, Jing Ren, Emanuele Rodolà, Abhishek Sharma, Peter Wonka, Maks Ovsjanikov
Our main observation is that high quality maps can be obtained even if the input correspondences are noisy or are encoded by a small number of coefficients in a spectral basis.
Graphics
1 code implementation • 6 Dec 2018 • Oshri Halimi, Or Litany, Emanuele Rodolà, Alex Bronstein, Ron Kimmel
The resulting learning model is class-agnostic, and is able to leverage any type of deformable geometric data for the training phase.
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.
1 code implementation • 27 Jul 2018 • Riccardo Marin, Simone Melzi, Emanuele Rodolà, Umberto Castellani
We introduce a new method for non-rigid registration of 3D human shapes.
1 code implementation • 25 Jul 2017 • Zorah Lähner, Matthias Vestner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Ron Kimmel, Daniel Cremers
We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality.
3 code implementations • ICCV 2017 • Or Litany, Tal Remez, Emanuele Rodolà, Alex M. Bronstein, Michael M. Bronstein
We introduce a new framework for learning dense correspondence between deformable 3D shapes.
no code implementations • CVPR 2017 • Matthias Vestner, Roee Litman, Emanuele Rodolà, Alex Bronstein, Daniel Cremers
Many algorithms for the computation of correspondences between deformable shapes rely on some variant of nearest neighbor matching in a descriptor space.
4 code implementations • CVPR 2017 • Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein
Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.
Ranked #4 on Document Classification on Cora
no code implementations • 12 Jul 2016 • Matthias Vestner, Roee Litman, Alex Bronstein, Emanuele Rodolà, Daniel Cremers
Many algorithms for the computation of correspondences between deformable shapes rely on some variant of nearest neighbor matching in a descriptor space.
no code implementations • NeurIPS 2016 • Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein
Establishing correspondence between shapes is a fundamental problem in geometry processing, arising in a wide variety of applications.
no code implementations • CVPR 2016 • Zorah Lähner, Emanuele Rodolà, Frank R. Schmidt, Michael M. Bronstein, Daniel Cremers
We propose the first algorithm for non-rigid 2D-to-3D shape matching, where the input is a 2D shape represented as a planar curve and a 3D shape represented as a surface; the output is a continuous curve on the surface.
no code implementations • 18 Jun 2015 • Emanuele Rodolà, Michael Moeller, Daniel Cremers
Since their introduction in the shape analysis community, functional maps have met with considerable success due to their ability to compactly represent dense correspondences between deformable shapes, with applications ranging from shape matching and image segmentation, to exploration of large shape collections.
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.