4 code implementations • 3 May 2015 • Francesco Visin, Kyle Kastner, Kyunghyun Cho, Matteo Matteucci, Aaron Courville, Yoshua Bengio
In this paper, we propose a deep neural network architecture for object recognition based on recurrent neural networks.
Ranked #34 on Image Classification on MNIST
no code implementations • 20 Jul 2015 • Andrea Romanoni, Matteo Matteucci
From the 3D Delaunay triangulation of these points, state-of-the-art algorithms build a manifold rough model of the scene.
2 code implementations • 22 Nov 2015 • Francesco Visin, Marco Ciccone, Adriana Romero, Kyle Kastner, Kyunghyun Cho, Yoshua Bengio, Matteo Matteucci, Aaron Courville
Moreover, ReNet layers are stacked on top of pre-trained convolutional layers, benefiting from generic local features.
Ranked #18 on Semantic Segmentation on CamVid
no code implementations • 21 Apr 2016 • Andrea Romanoni, Matteo Matteucci
Urban reconstruction from a video captured by a surveying vehicle constitutes a core module of automated mapping.
no code implementations • 21 Apr 2016 • Andrea Romanoni, Amaël Delaunoy, Marc Pollefeys, Matteo Matteucci
In this paper we propose a new approach to incrementally initialize a manifold surface for automatic 3D reconstruction from images.
no code implementations • 30 Jul 2016 • Yashar Deldjoo, Shengping Zhang, Bahman Zanj, Paolo Cremonesi, Matteo Matteucci
Recently, sparse representation based visual tracking methods have attracted increasing attention in the computer vision community.
no code implementations • 29 Sep 2016 • Gheorghii Postica, Andrea Romanoni, Matteo Matteucci
Detecting moving objects in dynamic scenes from sequences of lidar scans is an important task in object tracking, mapping, localization, and navigation.
no code implementations • 16 Aug 2017 • Andrea Romanoni, Marco Ciccone, Francesco Visin, Matteo Matteucci
In this paper we propose a novel method to refine both the geometry and the semantic labeling of a given mesh.
no code implementations • 18 Aug 2017 • Andrea Romanoni, Daniele Fiorenti, Matteo Matteucci
In the era of autonomous driving, urban mapping represents a core step to let vehicles interact with the urban context.
1 code implementation • 17 Jan 2018 • Andrea Bignoli, Andrea Romanoni, Matteo Matteucci
This paper presents a novel method for the reconstruction of 3D edges in multi-view stereo scenarios.
1 code implementation • 16 May 2018 • Luca Morreale, Andrea Romanoni, Matteo Matteucci
Finding the best poses to capture part of the scene is one of the most challenging topic that goes under the name of Next Best View.
no code implementations • 21 May 2018 • Marco Cannici, Marco Ciccone, Andrea Romanoni, Matteo Matteucci
Event-based cameras, also known as neuromorphic cameras, are bioinspired sensors able to perceive changes in the scene at high frequency with low power consumption.
no code implementations • 14 Jun 2018 • Francesco Lattari, Marco Ciccone, Matteo Matteucci, Jonathan Masci, Francesco Visin
We introduce ReConvNet, a recurrent convolutional architecture for semi-supervised video object segmentation that is able to fast adapt its features to focus on any specific object of interest at inference time.
no code implementations • 25 Jul 2018 • Marco Cannici, Marco Ciccone, Andrea Romanoni, Matteo Matteucci
Event-based cameras are neuromorphic sensors capable of efficiently encoding visual information in the form of sparse sequences of events.
no code implementations • 26 Jul 2018 • Andrea Romanoni, Matteo Matteucci
Mesh labeling is the key problem of classifying the facets of a 3D mesh with a label among a set of possible ones.
no code implementations • 20 Feb 2019 • Luca Morreale, Andrea Romanoni, Matteo Matteucci
Dense 3D visual mapping estimates as many as possible pixel depths, for each image.
no code implementations • ICCV 2019 • Andrea Romanoni, Matteo Matteucci
One of the most successful approaches in Multi-View Stereo estimates a depth map and a normal map for each view via PatchMatch-based optimization and fuses them into a consistent 3D points cloud.
no code implementations • 21 May 2019 • Andrea Romanoni, Matteo Matteucci
Many Multi-View-Stereo algorithms extract a 3D mesh model of a scene, after fusing depth maps into a volumetric representation of the space.
no code implementations • 10 Jul 2019 • Luca Cavalli, Gianpaolo Di Pietro, Matteo Matteucci
Indeed, we assess the validity of our novel framework both in the context of perfect information, i. e., known object model, and in the partial information context, i. e., inferring task oriented metrics from vision, underlining advantages and limitations of both situations.
1 code implementation • ECCV 2020 • Marco Cannici, Marco Ciccone, Andrea Romanoni, Matteo Matteucci
Dynamic Vision Sensors (DVSs) asynchronously stream events in correspondence of pixels subject to brightness changes.
1 code implementation • 17 Aug 2020 • Chiara Plizzari, Marco Cannici, Matteo Matteucci
Skeleton-based Human Activity Recognition has achieved great interest in recent years as skeleton data has demonstrated being robust to illumination changes, body scales, dynamic camera views, and complex background.
no code implementations • 29 Sep 2020 • Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli
Hybrid system identification is a key tool to achieve reliable models of Cyber-Physical Systems from data.
no code implementations • 29 Sep 2020 • Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli
This work targets the identification of a class of models for hybrid dynamical systems characterized by nonlinear autoregressive exogenous (NARX) components, with finite-dimensional polynomial expansions, and by a Markovian switching mechanism.
no code implementations • 1 Dec 2020 • Andrea Romanoni, Matteo Matteucci
The refinement step is applied for each facet using only the camera pair selected.
1 code implementation • 11 Dec 2020 • Chiara Plizzari, Marco Cannici, Matteo Matteucci
Skeleton-based human action recognition has achieved a great interest in recent years, as skeleton data has been demonstrated to be robust to illumination changes, body scales, dynamic camera views, and complex background.
no code implementations • 23 Dec 2020 • Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli, Andrea Vitali
Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids.
no code implementations • 23 Mar 2021 • Mirco Planamente, Chiara Plizzari, Marco Cannici, Marco Ciccone, Francesco Strada, Andrea Bottino, Matteo Matteucci, Barbara Caputo
Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events".
2 code implementations • 4 May 2021 • Alberto Archetti, Marco Cannici, Matteo Matteucci
Recently, the trend of incorporating differentiable algorithms into deep learning architectures arose in machine learning research, as the fusion of neural layers and algorithmic layers has been beneficial for handling combinatorial data, such as shortest paths on graphs.
no code implementations • 28 Jul 2021 • Eugenio Lomurno, Andrea Romanoni, Matteo Matteucci
Today, Multi-View Stereo techniques are able to reconstruct robust and detailed 3D models, especially when starting from high-resolution images.
no code implementations • 31 Aug 2021 • Lorenzo Cazzella, Dario Tagliaferri, Marouan Mizmizi, Damiano Badini, Christian Mazzucco, Matteo Matteucci, Umberto Spagnolini
Algebraic Low-rank (LR) channel estimation exploits space-time channel sparsity through the computation of position-dependent MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell.
no code implementations • 24 Sep 2021 • Eugenio Lomurno, Alberto Archetti, Lorenzo Cazzella, Stefano Samele, Leonardo Di Perna, Matteo Matteucci
In this context, federated learning is one of the most influential frameworks for privacy-preserving distributed machine learning, achieving astounding results in many natural language processing and computer vision tasks.
no code implementations • 29 Sep 2021 • Stefano Samele, Matteo Matteucci
In this work, we investigate a methodology to perform anomaly detection and localization on images.
1 code implementation • 7 Dec 2021 • Chiara Plizzari, Mirco Planamente, Gabriele Goletto, Marco Cannici, Emanuele Gusso, Matteo Matteucci, Barbara Caputo
However, the ever-growing field of event-based vision has, to date, overlooked the potential of event cameras in such applications.
1 code implementation • CVPR 2022 • Chiara Plizzari, Mirco Planamente, Gabriele Goletto, Marco Cannici, Emanuele Gusso, Matteo Matteucci, Barbara Caputo
However, the ever-growing field of event-based vision has, to date, overlooked the potential of event cameras in such applications.
1 code implementation • 21 Jun 2022 • Davide Tateo, Davide Antonio Cucci, Matteo Matteucci, Andrea Bonarini
In this paper, we propose the use of an efficient representation, based on structural points, for the geometry of objects to be used as landmarks in a monocular semantic SLAM system based on the pose-graph formulation.
no code implementations • 7 Sep 2022 • Eugenio Lomurno, Matteo Matteucci
According to the literature, this approach has proven to be a successful defence against several models' privacy attacks, but its downside is a substantial degradation of the models' performance.
no code implementations • 6 Oct 2022 • Andrea Falanti, Eugenio Lomurno, Stefano Samele, Danilo Ardagna, Matteo Matteucci
With its sequential model-based optimization strategy, Progressive Neural Architecture Search (PNAS) represents a possible step forward to face this resources issue.
no code implementations • 20 Nov 2022 • Cristian Sbrolli, Paolo Cudrano, Matteo Frosi, Matteo Matteucci
To address this limitation and enhance image-guided 3D DDPMs with augmented 3D understanding, we introduce CISP (Contrastive Image-Shape Pre-training), obtaining a well-structured image-shape joint embedding space.
no code implementations • 13 Dec 2022 • Andrea Falanti, Eugenio Lomurno, Danilo Ardagna, Matteo Matteucci
The automated machine learning (AutoML) field has become increasingly relevant in recent years.
no code implementations • 28 Jan 2023 • Simone Sarti, Eugenio Lomurno, Matteo Matteucci
Today, artificial neural networks are the state of the art for solving a variety of complex tasks, especially in image classification.
2 code implementations • 28 Jan 2023 • Alberto Archetti, Eugenio Lomurno, Francesco Lattari, André Martin, Matteo Matteucci
However, the data needed to train survival models are often distributed, incomplete, censored, and confidential.
no code implementations • 3 Feb 2023 • Simone Sarti, Eugenio Lomurno, Andrea Falanti, Matteo Matteucci
The use of Neural Architecture Search (NAS) techniques to automate the design of neural networks has become increasingly popular in recent years.
1 code implementation • 6 Feb 2023 • Alberto Archetti, Matteo Matteucci
In this work, we present a novel federated algorithm for survival analysis based on one of the most successful survival models, the random survival forest.
no code implementations • 13 Mar 2023 • Lorenzo Cazzella, Francesco Linsalata, Maurizio Magarini, Matteo Matteucci, Umberto Spagnolini
Digital Twins (DTs) for physical wireless environments have been recently proposed as accurate virtual representations of the propagation environment that can enable multi-layer decisions at the physical communication equipment.
no code implementations • 10 Apr 2023 • Riccardo Ughi, Eugenio Lomurno, Matteo Matteucci
The Transformer is a highly successful deep learning model that has revolutionised the world of artificial neural networks, first in natural language processing and later in computer vision.
no code implementations • 12 Apr 2023 • Nico Catalano, Matteo Matteucci
Semantic segmentation assigns category labels to each pixel in an image, enabling breakthroughs in fields such as autonomous driving and robotics.
no code implementations • 17 May 2023 • Andrea Lampis, Eugenio Lomurno, Matteo Matteucci
These results represent a new state of the art in Classification Accuracy Score and highlight the effectiveness of post-processing techniques in improving the quality of synthetic datasets.
no code implementations • 5 Jun 2023 • Eugenio Lomurno, Alberto Archetti, Francesca Ausonio, Matteo Matteucci
The remarkable proliferation of deep learning across various industries has underscored the importance of data privacy and security in AI pipelines.
no code implementations • 3 Jul 2023 • Simone Sarti, Eugenio Lomurno, Matteo Matteucci
Deep learning is increasingly impacting various aspects of contemporary society.
1 code implementation • 3 Jul 2023 • Agnese Chiatti, Riccardo Bertoglio, Nico Catalano, Matteo Gatti, Matteo Matteucci
Mobile robots will play a crucial role in the transition towards sustainable agriculture.
no code implementations • 4 Aug 2023 • Alberto Archetti, Francesca Ieva, Matteo Matteucci
Our results underscore the potential of FedSurF++ to improve the scalability and effectiveness of survival analysis in distributed settings while preserving user privacy.
no code implementations • 5 Sep 2023 • Paolo Cudrano, Matteo Bellusci, Giuseppe Macino, Matteo Matteucci
Accurate road surface classification is crucial for autonomous vehicles (AVs) to optimize driving conditions, enhance safety, and enable advanced road mapping.
no code implementations • 13 Sep 2023 • Mirko Usuelli, Matteo Frosi, Paolo Cudrano, Simone Mentasti, Matteo Matteucci
The methodology undergoes evaluation across a variety of FMCW Radar dataset scenes, and it is compared to state-of-the-art systems such as Scan Context for Place Recognition and ICP for Loop Closure.
no code implementations • 29 Sep 2023 • Eugenio Lomurno, Simone Toffoli, Davide Di Febbo, Matteo Matteucci, Francesca Lunardini, Simona Ferrante
Content-free handwriting data from 80 healthy participants in different age groups (20-40, 41-60, 61-70 and 70+) were analysed.
no code implementations • 11 Jan 2024 • Lorenzo Cazzella, Marouan Mizmizi, Dario Tagliaferri, Damiano Badini, Matteo Matteucci, Umberto Spagnolini
Simulation results over different urban vehicular mobility scenarios show that the proposed T2U method provides a probability of correct association that increases with the size of the BS antenna array, highlighting the respective increase of the separability of the VEs in the beamspace.
no code implementations • 2 Feb 2024 • Cristian Sbrolli, Paolo Cudrano, Matteo Matteucci
We find that, while matching CLIP in generation quality and diversity, CISP substantially improves coherence with input images, underscoring the value of incorporating 3D knowledge into generative models.
no code implementations • 9 Feb 2024 • Nico Catalano, Alessandro Maranelli, Agnese Chiatti, Matteo Matteucci
\acrlong{fss}, in particular, concerns the extension and optimization of traditional segmentation methods in challenging conditions where limited training examples are available.
no code implementations • 23 Feb 2024 • Fatemeh Baghdadi, Davide Cirillo, Daniele Lezzi, Francesc Lordan, Fernando Vazquez, Eugenio Lomurno, Alberto Archetti, Danilo Ardagna, Matteo Matteucci
The AI-SPRINT project, launched in 2021 and funded by the European Commission, focuses on the development and implementation of AI applications across the computing continuum.
1 code implementation • 29 Feb 2024 • Giuseppe Chiari, Davide Galli, Francesco Lattari, Matteo Matteucci, Davide Zoni
Side-channel attacks allow extracting secret information from the execution of cryptographic primitives by correlating the partially known computed data and the measured side-channel signal.
no code implementations • 22 Mar 2024 • Andrea Menta, Alberto Archetti, Matteo Matteucci
Neural cellular automata represent an evolution of the traditional cellular automata model, enhanced by the integration of a deep learning-based transition function.
no code implementations • 4 May 2024 • Eugenio Lomurno, Matteo D'Oria, Matteo Matteucci
Recent advances in generative artificial intelligence have enabled the creation of high-quality synthetic data that closely mimics real-world data.
no code implementations • 6 May 2024 • Lunchen Xie, Eugenio Lomurno, Matteo Gambella, Danilo Ardagna, Manuel Roveri, Matteo Matteucci, Qingjiang Shi
Accurate classification of medical images is essential for modern diagnostics.