Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Cosmo, Luca"'
Publikováno v:
ECCV 2024
Unsupervised 3D keypoints estimation from Point Cloud Data (PCD) is a complex task, even more challenging when an object shape is deforming. As keypoints should be semantically and geometrically consistent across all the 3D frames - each keypoint sho
Externí odkaz:
http://arxiv.org/abs/2408.02291
Autor:
Postolache, Emilian, Polouliakh, Natalia, Kitano, Hiroaki, Connelly, Akima, Rodolà, Emanuele, Cosmo, Luca, Akama, Taketo
In this article, we explore the potential of using latent diffusion models, a family of powerful generative models, for the task of reconstructing naturalistic music from electroencephalogram (EEG) recordings. Unlike simpler music with limited timbre
Externí odkaz:
http://arxiv.org/abs/2405.09062
Autor:
Ciranni, Ruben, Postolache, Emilian, Mariani, Giorgio, Mancusi, Michele, Cosmo, Luca, Rodolà, Emanuele
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. Our method operates at the level of stems (or th
Externí odkaz:
http://arxiv.org/abs/2404.16969
Multi-Source Diffusion Models (MSDM) allow for compositional musical generation tasks: generating a set of coherent sources, creating accompaniments, and performing source separation. Despite their versatility, they require estimating the joint distr
Externí odkaz:
http://arxiv.org/abs/2403.11706
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. Specifically, we propose to use a denoising model to sample eigenvectors and eigenvalues
Externí odkaz:
http://arxiv.org/abs/2402.18974
Autor:
Mariani, Giorgio, Tallini, Irene, Postolache, Emilian, Mancusi, Michele, Cosmo, Luca, Rodolà, Emanuele
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. Alongside the classic total inference tasks (i.e., g
Externí odkaz:
http://arxiv.org/abs/2302.02257
Autor:
Postolache, Emilian, Mariani, Giorgio, Mancusi, Michele, Santilli, Andrea, Cosmo, Luca, Rodolà, Emanuele
Autoregressive models have achieved impressive results over a wide range of domains in terms of generation quality and downstream task performance. In the continuous domain, a key factor behind this success is the usage of quantized latent spaces (e.
Externí odkaz:
http://arxiv.org/abs/2301.08562
Autor:
Pegoraro, Marco, Marin, Riccardo, Rampini, Arianna, Melzi, Simone, Cosmo, Luca, Rodolà, Emanuele
In graph learning, maps between graphs and their subgraphs frequently arise. For instance, when coarsening or rewiring operations are present along the pipeline, one needs to keep track of the corresponding nodes between the original and modified gra
Externí odkaz:
http://arxiv.org/abs/2205.14938
Autor:
Mullakaeva, Kamilia, Cosmo, Luca, Kazi, Anees, Ahmadi, Seyed-Ahmad, Navab, Nassir, Bronstein, Michael M.
Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property prediction and brain connectome analysis. Importantly, recent works have show
Externí odkaz:
http://arxiv.org/abs/2204.00323
Shape matching has been a long-studied problem for the computer graphics and vision community. The objective is to predict a dense correspondence between meshes that have a certain degree of deformation. Existing methods either consider the local des
Externí odkaz:
http://arxiv.org/abs/2202.01537