Pietro Zanuttigh | Associate Professor

@inproceedings{rizzoli2025cars,

title = {When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather},

author = {Giulia Rizzoli and Matteo Caligiuri and Donald Shenaj and Francesco Barbato and Pietro Zanuttigh},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},

journal = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {HoloADMM: High-Quality Holographic Complex Field Recovery},

author = {Mazen Mel and Paul Springer and Pietro Zanuttigh and Zhou Haitao and Alexander Gatto},

url = {https://medialab.dei.unipd.it/paper_data/HoloADMM/paper.pdf},

year  = {2024},

date = {2024-10-01},

urldate = {2024-10-01},

booktitle = {Proceedings of European Conference on Computer Vision (ECCV)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{barbato2023continualb,

title = {Continual Road-Scene Semantic Segmentation via Feature-Aligned Symmetric Multi-Modal Network},

author = {Francesco Barbato and Elena Camuffo and Simone Milani and Pietro Zanuttigh},

url = {https://arxiv.org/pdf/2308.04702.pdf},

year  = {2024},

date = {2024-06-25},

urldate = {2024-06-25},

booktitle = {IEEE International Conference on Image Processing (ICIP)},

journal = {arXiv preprint arXiv:2308.04702},

abstract = {State-of-the-art multimodal semantic segmentation approaches combining LiDAR and color data are usually designed on top of asymmetric information-sharing schemes and assume that both modalities are always available. Regrettably, this strong assumption may not hold in real-world scenarios, where sensors are prone to failure or can face adverse conditions (night-time, rain, fog, etc.) that make the acquired information unreliable. Moreover, these architectures tend to fail in continual learning scenarios. 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. This makes our model reliable even in safetycritical settings, as is the case of autonomous driving. We evaluate our approach on the SemanticKITTI dataset, comparing it with our closest competitor. We also introduce an ad-hoc continual learning scheme and show results in a class-incremental continual learning scenario that prove the effectiveness of the approach also in this setting.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{martinturrero2024alerttransformer,

title = {ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data},

author = {Carmen Martin-Turrero and Maxence Bouvier and Manuel Breitenstein and Pietro Zanuttigh and Vincent Parret},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{liu2024learning,

title = {Learning from the Web: Language Drives Weakly-Supervised Incremental Learning for Semantic Segmentation},

author = {Chang Liu and Giulia Rizzoli and Pietro Zanuttigh and Fu Li and Yi Niu},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {European Conference on Computer Vision},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rizzoli2024source,

title = {Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers},

author = {Giulia Rizzoli and Donald Shenaj and Pietro Zanuttigh},

url = {https://openaccess.thecvf.com/content/WACV2024W/Pretrain/html/Rizzoli_Source-Free_Domain_Adaptation_for_RGB-D_Semantic_Segmentation_With_Vision_Transformers_WACVW_2024_paper.html},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},

pages = {615–624},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3625468.3647623,

title = {A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation},

author = {Francesco Barbato and Umberto Michieli and Mehmet Kerim Yucel and Pietro Zanuttigh and Mete Ozay},

url = {https://doi.org/10.1145/3625468.3647623},

doi = {10.1145/3625468.3647623},

isbn = {9798400704123},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference},

pages = {190–201},

publisher = {Association for Computing Machinery},

address = {, Bari, Italy,},

series = {MMSys '24},

abstract = {Performance degradation caused by corrupted multimedia samples is a critical challenge for machine learning models. Previously, three groups of approaches have been proposed to tackle this issue: i) enhancer and denoiser modules to improve the quality of the noisy data, ii) data augmentation approaches, and iii) domain adaptation strategies. All have drawbacks limiting applicability; the first requires paired clean-corrupted data for training and has an high computational cost, while the others can only be used on the same task they were trained on. In this paper, we propose SyMPIE to solve these shortcomings, designing a small, modular, and efficient system to enhance input data for robust downstream multimedia understanding with minimal computational cost. Our SyMPIE is pre-trained on an upstream task/network that should not match the downstream ones and does not need paired clean-corrupted samples. Our key insight is that most input corruptions found in real-world tasks can be modeled through global operations on color channels of images or spatial filters with small kernels. We validate our approach on multiple datasets and tasks, such as image classification (on ImageNetC, ImageNetC-Bar, VizWiz, and a newly proposed mixed corruption benchmark named ImageNetC-mixed) and semantic segmentation (on Cityscapes, ACDC, and DarkZurich) with consistent improvements of about 5% relative accuracy gain across the board1.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lincetto2023,

title = {Exploiting Multiple Priors for Neural 3D Indoor Reconstruction},

author = {Federico Lincetto and Gianluca Agresti and Mattia Rossi and Pietro Zanuttigh},

doi = {https://doi.org/10.48550/arXiv.2309.07021},

year  = {2023},

date = {2023-11-20},

booktitle = {34th British Machine Vision Conference},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Shenaj2023,

title = {Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning},

author = {Donald Shenaj and Eros Fan`i and Marco Toldo and Debora Caldarola and Antonio Tavera and Umberto Michieli and Marco Ciccone and Pietro Zanuttigh and Barbara Caputo},

year  = {2023},

date = {2023-01-01},

booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{barbato2023depthformer,

title = {DepthFormer: Multimodal Positional Encodings and Cross-Input Attention for Transformer-Based Segmentation Networks},

author = {Francesco Barbato and Giulia Rizzoli and Pietro Zanuttigh},

year  = {2023},

date = {2023-01-01},

booktitle = {ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{amann2023material,

title = {Material Characterization using a Compact Computed Tomography Imaging Spectrometer with Super-resolution Capability},

author = {Simon Amann and Mazen Mel and Pietro Zanuttigh and Tobias Haist and Markus Kamm and Alexander Gatto and others},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Proceedings of the 6th International Conference on Optical Characterization of Materials, OCM 2023},

pages = {139–148},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{shenaj2023asynchronous,

title = {Asynchronous Federated Continual Learning},

author = {Donald Shenaj and Marco Toldo and Alberto Rigon and Pietro Zanuttigh},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {CVPR FedVision Workshop},

journal = {arXiv preprint arXiv:2304.03626},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rizzoli2023syndrone,

title = {SynDrone-Multi-Modal UAV Dataset for Urban Scenarios},

author = {Giulia Rizzoli and Francesco Barbato and Matteo Caligiuri and Pietro Zanuttigh},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},

pages = {2210–2220},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Jiang2022,

title = {A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps},

author = {Xiaowen Jiang and Valerio Cambareri and Gianluca Agresti and Cynthia Ifeyinwa Ugwu and Adriano Simonetto and Fabien Cardinaux and Pietro Zanuttigh},

year  = {2022},

date = {2022-01-01},

booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},

pages = {2687--2696},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Casarin2022,

title = {Unsupervised Network Anomaly Detection by Learning on 2D Data Representations},

author = {Sofia Casarin and Sara Baldoni and Marco Carli and Pietro Zanuttigh and Federica Battisti},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 9th Swiss Conference on Data Science (SDS)},

pages = {53--58},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Barbato2021,

title = {Latent space regularization for unsupervised domain adaptation in semantic segmentation},

author = {Francesco Barbato and Marco Toldo and Umberto Michieli and Pietro Zanuttigh},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},

pages = {2835--2845},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Spadotto2021,

title = {Unsupervised domain adaptation with multiple domain discriminators and adaptive self-training},

author = {Teo Spadotto and Marco Toldo and Umberto Michieli and Pietro Zanuttigh},

year  = {2021},

date = {2021-01-01},

booktitle = {2020 25th International Conference on Pattern Recognition (ICPR)},

pages = {2845--2852},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Toldo2021,

title = {Unsupervised domain adaptation in semantic segmentation via orthogonal and clustered embeddings},

author = {Marco Toldo and Umberto Michieli and Pietro Zanuttigh},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},

pages = {1358--1368},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Michieli2021a,

title = {Continual semantic segmentation via repulsion-attraction of sparse and disentangled latent representations},

author = {Umberto Michieli and Pietro Zanuttigh},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},

pages = {1114--1124},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Maracani2021,

title = {Recall: Replay-based continual learning in semantic segmentation},

author = {Andrea Maracani and Umberto Michieli and Marco Toldo and Pietro Zanuttigh},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},

pages = {7026--7035},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Simonetto2021,

title = {Semi-supervised deep learning techniques for spectrum reconstruction},

author = {Adriano Simonetto and Pietro Zanuttigh and Vincent Parret and Piergiorgio Sartor and Alexander Gatto},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {2020 25th International Conference on Pattern Recognition (ICPR)},

pages = {7767--7774},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Michieli2020a,

title = {Gmnet: Graph matching network for large scale part semantic segmentation in the wild},

author = {Umberto Michieli and Edoardo Borsato and Luca Rossi and Pietro Zanuttigh},

year  = {2020},

date = {2020-01-01},

booktitle = {Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part VIII 16},

pages = {397--414},

organization = {Springer International Publishing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Agresti2019a,

title = {Unsupervised domain adaptation for tof data denoising with adversarial learning},

author = {Gianluca Agresti and Henrik Schaefer and Piergiorgio Sartor and Pietro Zanuttigh},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},

pages = {5584--5593},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Biasetton2019,

title = {Unsupervised domain adaptation for semantic segmentation of urban scenes},

author = {Matteo Biasetton and Umberto Michieli and Gianluca Agresti and Pietro Zanuttigh},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},

pages = {0--0},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}