1 code implementation • 28 Mar 2024 • Matteo Caligiuri, Adriano Simonetto, Gianluca Agresti, Pietro Zanuttigh
The acquisition of objects outside the Line-of-Sight of cameras is a very intriguing but also extremely challenging research topic.
no code implementations • 20 Mar 2024 • Giulia Rizzoli, Matteo Caligiuri, Donald Shenaj, Francesco Barbato, Pietro Zanuttigh
In Federated Learning (FL), multiple clients collaboratively train a global model without sharing private data.
1 code implementation • 28 Feb 2024 • Francesco Barbato, Umberto Michieli, Mehmet Kerim Yucel, Pietro Zanuttigh, Mete Ozay
To this end, we design a small, modular, and efficient (just 2GFLOPs to process a Full HD image) system to enhance input data for robust downstream multimedia understanding with minimal computational cost.
no code implementations • 2 Feb 2024 • Carmen Martin-Turrero, Maxence Bouvier, Manuel Breitenstein, Pietro Zanuttigh, Vincent Parret
We propose a novel hybrid pipeline composed of asynchronous sensing and synchronous processing that combines several ideas: (1) an embedding based on PointNet models -- the ALERT module -- that can continuously integrate new and dismiss old events thanks to a leakage mechanism, (2) a flexible readout of the embedded data that allows to feed any downstream model with always up-to-date features at any sampling rate, (3) exploiting the input sparsity in a patch-based approach inspired by Vision Transformer to optimize the efficiency of the method.
Ranked #9 on Gesture Recognition on DVS128 Gesture
no code implementations • 19 Sep 2023 • Chang Liu, Giulia Rizzoli, Francesco Barbato, Andrea Maracani, Marco Toldo, Umberto Michieli, Yi Niu, Pietro Zanuttigh
Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies.
no code implementations • 13 Sep 2023 • Federico Lincetto, Gianluca Agresti, Mattia Rossi, Pietro Zanuttigh
In this work, we propose a novel neural implicit modeling method that leverages multiple regularization strategies to achieve better reconstructions of large indoor environments, while relying only on images.
1 code implementation • 21 Aug 2023 • Giulia Rizzoli, Francesco Barbato, Matteo Caligiuri, Pietro Zanuttigh
The development of computer vision algorithms for Unmanned Aerial Vehicles (UAVs) imagery heavily relies on the availability of annotated high-resolution aerial data.
no code implementations • 9 Aug 2023 • Francesco Barbato, Elena Camuffo, Simone Milani, Pietro Zanuttigh
In this work, we re-frame the task of multimodal semantic segmentation by enforcing a tightly-coupled feature representation and a symmetric information-sharing scheme, which allows our approach to work even when one of the input modalities is missing.
no code implementations • 23 May 2023 • Giulia Rizzoli, Donald Shenaj, Pietro Zanuttigh
With the increasing availability of depth sensors, multimodal frameworks that combine color information with depth data are gaining interest.
1 code implementation • 7 Apr 2023 • Donald Shenaj, Marco Toldo, Alberto Rigon, Pietro Zanuttigh
We introduce a novel federated learning setting (AFCL) where the continual learning of multiple tasks happens at each client with different orderings and in asynchronous time slots.
no code implementations • 8 Nov 2022 • Francesco Barbato, Giulia Rizzoli, Pietro Zanuttigh
Most approaches for semantic segmentation use only information from color cameras to parse the scenes, yet recent advancements show that using depth data allows to further improve performances.
no code implementations • 13 Oct 2022 • Marco Toldo, Umberto Michieli, Pietro Zanuttigh
Then, we address the proposed setup by using style transfer techniques to extend knowledge across domains when learning incremental tasks and a robust distillation framework to effectively recollect task knowledge under incremental domain shift.
1 code implementation • 5 Oct 2022 • Donald Shenaj, Eros Fanì, Marco Toldo, Debora Caldarola, Antonio Tavera, Umberto Michieli, Marco Ciccone, Pietro Zanuttigh, Barbara Caputo
Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the collected data.
no code implementations • 25 May 2022 • Xiaowen Jiang, Valerio Cambareri, Gianluca Agresti, Cynthia Ifeyinwa Ugwu, Adriano Simonetto, Fabien Cardinaux, Pietro Zanuttigh
We also achieve low memory footprint for weights and activations by means of mixed precision quantization-at-training techniques.
no code implementations • 20 Apr 2022 • Paolo Testolina, Francesco Barbato, Umberto Michieli, Marco Giordani, Pietro Zanuttigh, Michele Zorzi
Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems.
no code implementations • 14 Apr 2022 • Mazen Mel, Muhammad Siddiqui, Pietro Zanuttigh
Monocular depth estimation is still an open challenge due to the ill-posed nature of the problem at hand.
Ranked #1 on Monocular Depth Estimation on SUN-RGBD
no code implementations • 18 Jan 2022 • Donald Shenaj, Francesco Barbato, Umberto Michieli, Pietro Zanuttigh
In this paper we introduce the novel task of coarse-to-fine learning of semantic segmentation architectures in presence of domain shift.
no code implementations • 29 Nov 2021 • Adriano Simonetto, Gianluca Agresti, Pietro Zanuttigh, Henrik Schäfer
Indirect Time-of-Flight cameras (iToF) are low-cost devices that provide depth images at an interactive frame rate.
1 code implementation • ICCV 2021 • Andrea Maracani, Umberto Michieli, Marco Toldo, Pietro Zanuttigh
Replay data are then blended with new samples during the incremental steps.
1 code implementation • 6 Aug 2021 • Francesco Barbato, Umberto Michieli, Marco Toldo, Pietro Zanuttigh
Deep learning models obtain impressive accuracy in road scenes understanding, however they need a large quantity of labeled samples for their training.
1 code implementation • 6 Apr 2021 • Francesco Barbato, Marco Toldo, Umberto Michieli, Pietro Zanuttigh
Deep convolutional neural networks for semantic segmentation achieve outstanding accuracy, however they also have a couple of major drawbacks: first, they do not generalize well to distributions slightly different from the one of the training data; second, they require a huge amount of labeled data for their optimization.
no code implementations • CVPR 2021 • Umberto Michieli, Pietro Zanuttigh
Second, features sparsification allows to make room in the latent space to accommodate novel classes.
Ranked #3 on Disjoint 15-5 on PASCAL VOC 2012
1 code implementation • 25 Nov 2020 • Marco Toldo, Umberto Michieli, Pietro Zanuttigh
Deep learning frameworks allowed for a remarkable advancement in semantic segmentation, but the data hungry nature of convolutional networks has rapidly raised the demand for adaptation techniques able to transfer learned knowledge from label-abundant domains to unlabeled ones.
no code implementations • ECCV 2020 • Umberto Michieli, Edoardo Borsato, Luca Rossi, Pietro Zanuttigh
To tackle part-level ambiguity and localization we propose a novel adjacency graph-based module that aims at matching the relative spatial relationships between ground truth and predicted parts.
Ranked #7 on Semantic Segmentation on FMB Dataset
no code implementations • 21 May 2020 • Marco Toldo, Andrea Maracani, Umberto Michieli, Pietro Zanuttigh
The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation.
no code implementations • 27 Apr 2020 • Teo Spadotto, Marco Toldo, Umberto Michieli, Pietro Zanuttigh
We introduce a novel UDA framework where a standard supervised loss on labeled synthetic data is supported by an adversarial module and a self-training strategy aiming at aligning the two domain distributions.
no code implementations • 14 Jan 2020 • Marco Toldo, Umberto Michieli, Gianluca Agresti, Pietro Zanuttigh
The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data.
no code implementations • 8 Nov 2019 • Umberto Michieli, Pietro Zanuttigh
Deep learning architectures have shown remarkable results in scene understanding problems, however they exhibit a critical drop of performances when they are required to learn incrementally new tasks without forgetting old ones.
no code implementations • 2 Sep 2019 • Umberto Michieli, Matteo Biasetton, Gianluca Agresti, Pietro Zanuttigh
A recently proposed workaround is to train deep networks using synthetic data, but the domain shift between real world and synthetic representations limits the performance.
2 code implementations • 31 Jul 2019 • Umberto Michieli, Pietro Zanuttigh
To tackle this task we propose to distill the knowledge of the previous model to retain the information about previously learned classes, whilst updating the current model to learn the new ones.
Ranked #4 on Domain 11-1 on Cityscapes