Diego Valsesia | DeepAI

AI Chat

Featured Co-authors

Luisa Verdoliva
37 publications
Tatiana Tommasi
32 publications
Enrico Magli
29 publications
Giovanni Poggi
19 publications
Giulia Fracastoro
19 publications
Giuseppe Scarpa
7 publications
Chiara Ravazzi
4 publications
Antonio Montanaro
4 publications
Tiziano Bianchi
4 publications
Antonio Alliegro
4 publications
Andrea Bordone Molini
4 publications

∙ 01/19/2023

Fast Inference in Denoising Diffusion Models via MMD Finetuning

Denoising Diffusion Models (DDMs) have become a popular tool for generat...

0 Emanuele Aiello, et al. ∙

∙ 09/21/2022

Rethinking the compositionality of point clouds through regularization in the hyperbolic space

Point clouds of 3D objects exhibit an inherent compositional nature wher...

0 Antonio Montanaro, et al. ∙

∙ 09/20/2022

Cross-modal Learning for Image-Guided Point Cloud Shape Completion

In this paper we explore the recent topic of point cloud completion, gui...

1 Emanuele Aiello, et al. ∙

∙ 07/01/2022

Exploring the solution space of linear inverse problems with GAN latent geometry

Inverse problems consist in reconstructing signals from incomplete sets ...

0 Antonio Montanaro, et al. ∙

∙ 04/06/2022

Super-resolved multi-temporal segmentation with deep permutation-invariant networks

Multi-image super-resolution from multi-temporal satellite acquisitions ...

0 Diego Valsesia, et al. ∙

∙ 08/20/2021

Self-supervised learning for joint SAR and multispectral land cover classification

Self-supervised learning techniques are gaining popularity due to their ...

0 Antonio Montanaro, et al. ∙

∙ 05/26/2021

Permutation invariance and uncertainty in multitemporal image super-resolution

Recent advances have shown how deep neural networks can be extremely eff...

6 Diego Valsesia, et al. ∙

∙ 03/30/2021

Denoise and Contrast for Category Agnostic Shape Completion

In this paper, we present a deep learning model that exploits the power ...

0 Antonio Alliegro, et al. ∙

∙ 03/29/2021

RAN-GNNs: breaking the capacity limits of graph neural networks

Graph neural networks have become a staple in problems addressing learni...

0 Diego Valsesia, et al. ∙

∙ 12/10/2020

Deep learning methods for SAR image despeckling: trends and perspectives

Synthetic aperture radar (SAR) images are affected by a spatially-correl...

0 Giulia Fracastoro, et al. ∙

∙ 07/06/2020

Learning Graph-Convolutional Representations for Point Cloud Denoising

Point clouds are an increasingly relevant data type but they are often c...

0 Francesca Pistilli, et al. ∙

∙ 07/04/2020

Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks

Information extraction from synthetic aperture radar (SAR) images is hea...

6 Andrea Bordone Molini, et al. ∙

∙ 01/15/2020

Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks

SAR despeckling is a problem of paramount importance in remote sensing, ...

0 Andrea Bordone Molini, et al. ∙

∙ 01/15/2020

DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images

Deep learning methods for super-resolution of a remote sensing scene fro...

0 Andrea Bordone Molini, et al. ∙

∙ 09/02/2019

Analysis of SparseHash: an efficient embedding of set-similarity via sparse projections

Embeddings provide compact representations of signals in order to perfor...

0 Diego Valsesia, et al. ∙

∙ 07/19/2019

Deep Graph-Convolutional Image Denoising

Non-local self-similarity is well-known to be an effective prior for the...

4 Diego Valsesia, et al. ∙

∙ 07/15/2019

DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images

Recently, convolutional neural networks (CNN) have been successfully app...

1 Andrea Bordone Molini, et al. ∙

∙ 07/05/2019

High-throughput Onboard Hyperspectral Image Compression with Ground-based CNN Reconstruction

Compression of hyperspectral images onboard of spacecrafts is a tradeoff...

4 Diego Valsesia, et al. ∙

∙ 05/29/2019

Image Denoising with Graph-Convolutional Neural Networks

Recovering an image from a noisy observation is a key problem in signal ...

0 Diego Valsesia, et al. ∙

∙ 04/17/2018

Sampling of graph signals via randomized local aggregations

Sampling of signals defined over the nodes of a graph is one of the cruc...

0 Diego Valsesia, et al. ∙

∙ 10/08/2013

Smoothness-Constrained Image Recovery from Block-Based Random Projections

In this paper we address the problem of visual quality of images reconst...

0 Giulio Coluccia, et al. ∙

∙ 10/04/2013

Spatially Scalable Compressed Image Sensing with Hybrid Transform and Inter-layer Prediction Model

Compressive imaging is an emerging application of compressed sensing, de...

0 Diego Valsesia, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world