2025
Lincetto, Federico; Agresti, Gianluca; Rossi, Mattia; Zanuttigh, Pietro
MultimodalStudio: A Heterogeneous Sensor Dataset and Framework for Neural Rendering across Multiple Imaging Modalities Proceedings Article
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025.
@inproceedings{lincetto2025multimodalstudio,
title = {MultimodalStudio: A Heterogeneous Sensor Dataset and Framework for Neural Rendering across Multiple Imaging Modalities},
author = {Federico Lincetto and Gianluca Agresti and Mattia Rossi and Pietro Zanuttigh},
url = {https://lttm.github.io/MultimodalStudio/},
year = {2025},
date = {2025-06-11},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rizzoli, Giulia; Caligiuri, Matteo; Shenaj, Donald; Barbato, Francesco; Zanuttigh, Pietro
When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather Proceedings Article
In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025.
@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},
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Baldoni, Sara; Benhamadi, Salim; Chiariotti, Federico; Zorzi, Michele; Battisti, Federica
Movement- and Traffic-based User Identification in Commercial Virtual Reality Applications: Threats and Opportunities Proceedings Article
In: 2025 IEEE Conference Virtual Reality and 3D User Interfaces (VR), pp. 72-81, 2025.
@inproceedings{10937457,
title = {Movement- and Traffic-based User Identification in Commercial Virtual Reality Applications: Threats and Opportunities},
author = {Sara Baldoni and Salim Benhamadi and Federico Chiariotti and Michele Zorzi and Federica Battisti},
doi = {10.1109/VR59515.2025.00032},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {2025 IEEE Conference Virtual Reality and 3D User Interfaces (VR)},
pages = {72-81},
abstract = {With the unprecedented diffusion of virtual reality, the number of application scenarios is continuously growing. As commercial and gaming applications become pervasive, the need for the secure and convenient identification of users, often overlooked by the research in immersive media, is becoming more and more pressing. Networked scenarios such as Cloud gaming or cooperative virtual training and teleoperation require both a user-friendly and streamlined experience and user privacy and security. In this work, we investigate the possibility of identifying users from their movement patterns and data traffic traces while playing four commercial games, using a publicly available dataset. If, on the one hand, this paves the way for easy identification and automatic customization of the virtual reality content, it also represents a serious threat to users’ privacy due to network analysis-based fingerprinting. Based on this, we analyze the threats and opportunities for virtual reality users’ security and privacy.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
With the unprecedented diffusion of virtual reality, the number of application scenarios is continuously growing. As commercial and gaming applications become pervasive, the need for the secure and convenient identification of users, often overlooked by the research in immersive media, is becoming more and more pressing. Networked scenarios such as Cloud gaming or cooperative virtual training and teleoperation require both a user-friendly and streamlined experience and user privacy and security. In this work, we investigate the possibility of identifying users from their movement patterns and data traffic traces while playing four commercial games, using a publicly available dataset. If, on the one hand, this paves the way for easy identification and automatic customization of the virtual reality content, it also represents a serious threat to users’ privacy due to network analysis-based fingerprinting. Based on this, we analyze the threats and opportunities for virtual reality users’ security and privacy.
Baldoni, Sara; Battisti, Federica
Histogram-based network traffic representation for anomaly detection through PCA Journal Article
In: Computer Networks, pp. 111276, 2025, ISSN: 1389-1286.
@article{BALDONI2025111276,
title = {Histogram-based network traffic representation for anomaly detection through PCA},
author = {Sara Baldoni and Federica Battisti},
url = {https://www.sciencedirect.com/science/article/pii/S1389128625002440},
doi = {https://doi.org/10.1016/j.comnet.2025.111276},
issn = {1389-1286},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Computer Networks},
pages = {111276},
abstract = {The constant increase of the number of connected devices, as well as of their heterogeneity, has greatly expanded the security threat landscape. For this reason, the prompt and effective detection of network traffic anomalies has become critical. In this work, we propose a new network traffic representation that aims at providing a compact and constantly updated summary of the current network condition. In addition, we propose an anomaly detection method based on the Principal Component Analysis of the aforementioned network representation. The proposed method exploits one-second time windows of network traffic, thus allowing an immediate reaction to anomalies. It is completely unsupervised, thus enabling the detection of zero-day attacks, and it has a low computational complexity, thus reducing the required capabilities of the monitoring nodes. The performance analysis showed that the proposed approach achieves comparable results with respect to state-of-the-art methods.},
keywords = {},
pubstate = {published},
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The constant increase of the number of connected devices, as well as of their heterogeneity, has greatly expanded the security threat landscape. For this reason, the prompt and effective detection of network traffic anomalies has become critical. In this work, we propose a new network traffic representation that aims at providing a compact and constantly updated summary of the current network condition. In addition, we propose an anomaly detection method based on the Principal Component Analysis of the aforementioned network representation. The proposed method exploits one-second time windows of network traffic, thus allowing an immediate reaction to anomalies. It is completely unsupervised, thus enabling the detection of zero-day attacks, and it has a low computational complexity, thus reducing the required capabilities of the monitoring nodes. The performance analysis showed that the proposed approach achieves comparable results with respect to state-of-the-art methods.
Neri, Michael; Battisti, Federica
Low-Complexity Patch-Based No-Reference Point Cloud Quality Metric Exploiting Weighted Structure and Texture Features Journal Article
In: IEEE Transactions on Broadcasting, 2025.
@article{neri2025low,
title = {Low-Complexity Patch-Based No-Reference Point Cloud Quality Metric Exploiting Weighted Structure and Texture Features},
author = {Michael Neri and Federica Battisti},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {IEEE Transactions on Broadcasting},
publisher = {IEEE},
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Schiavo, Chiara; Camuffo, Elena; Milani, Simone
SAGE: Semantic-Driven Adaptive Gaussian Splatting in Extended Reality Conference
EUSIPCO, 2025.
@conference{nokey,
title = {SAGE: Semantic-Driven Adaptive Gaussian Splatting in Extended Reality},
author = {Chiara Schiavo and Elena Camuffo and Simone Milani},
url = {https://www.arxiv.org/pdf/2503.16747},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {EUSIPCO},
abstract = {3D Gaussian Splatting (3DGS) has significantly improved the efficiency and realism of three-dimensional scene visualization in several applications, ranging from robotics to eXtended Reality (XR). This work presents SAGE (Semantic- Driven Adaptive Gaussian Splatting in Extended Reality), a novel framework designed to enhance the user experience by dynamically adapting the Level of Detail (LOD) of different 3DGS objects identified via a semantic segmentation. Experimental results demonstrate how SAGE effectively reduces memory and computational overhead while keeping a desired target visual quality, thus providing a powerful optimization for interactive XR applications.},
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3D Gaussian Splatting (3DGS) has significantly improved the efficiency and realism of three-dimensional scene visualization in several applications, ranging from robotics to eXtended Reality (XR). This work presents SAGE (Semantic- Driven Adaptive Gaussian Splatting in Extended Reality), a novel framework designed to enhance the user experience by dynamically adapting the Level of Detail (LOD) of different 3DGS objects identified via a semantic segmentation. Experimental results demonstrate how SAGE effectively reduces memory and computational overhead while keeping a desired target visual quality, thus providing a powerful optimization for interactive XR applications.
2024
Mel, Mazen; Springer, Paul; Zanuttigh, Pietro; Haitao, Zhou; Gatto, Alexander
HoloADMM: High-Quality Holographic Complex Field Recovery Proceedings Article
In: Proceedings of European Conference on Computer Vision (ECCV), 2024.
@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 = {},
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Mel, Mazen; Gatto, Alexander; Zanuttigh, Pietro
Joint Reconstruction and Spatial Super-resolution of Hyper-Spectral CTIS Images via Multi-Scale Refinement Journal Article
In: IEEE Transactions on Computational Imaging, 2024.
@article{nokey,
title = {Joint Reconstruction and Spatial Super-resolution of Hyper-Spectral CTIS Images via Multi-Scale Refinement},
author = {Mazen Mel and Alexander Gatto and Pietro Zanuttigh},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10640287},
year = {2024},
date = {2024-08-20},
journal = {IEEE Transactions on Computational Imaging},
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Camuffo, Elena; Michieli, Umberto; Milani, Simone; Moon, Ji Joong; Ozay, Mete
Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statistics Proceedings Article
In: IEEE, (Ed.): IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024.
@inproceedings{nokey,
title = {Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statistics},
author = {Elena Camuffo and Umberto Michieli and Simone Milani and Ji Joong Moon and Mete Ozay},
editor = {IEEE},
url = {https://arxiv.org/abs/2407.06450},
year = {2024},
date = {2024-06-30},
urldate = {2024-06-30},
booktitle = { IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
journal = {IEEE},
abstract = {Developing a reliable vision system is a fundamental challenge for robotic technologies (e.g., indoor service robots and outdoor autonomous robots) which can ensure reliable navigation even in challenging environments such as adverse weather conditions (e.g., fog, rain), poor lighting conditions (e.g., over/under exposure), or sensor degradation (e.g., blurring, noise), and can guarantee high performance in safety-critical functions. Current solutions proposed to improve model robustness usually rely on generic data augmentation techniques or employ costly test-time adaptation methods. In addition, most approaches focus on addressing a single vision task (typically, image recognition) utilising synthetic data. In this paper, we introduce Per-corruption Adaptation of Normalization statistics (PAN) to enhance the model robustness of vision systems. Our approach entails three key components: (i) a corruption type identification module, (ii) dynamic adjustment of normalization layer statistics based on identified corruption type, and (iii) real-time update of these statistics according to input data. PAN can integrate seamlessly with any convolutional model for enhanced accuracy in several robot vision tasks. In our experiments, PAN obtains robust performance improvement on challenging real-world corrupted image datasets (e.g., OpenLoris, ExDark, ACDC), where most of the current solutions tend to fail. Moreover, PAN outperforms the baseline models by 20-30% on synthetic benchmarks in object recognition tasks.},
keywords = {},
pubstate = {published},
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Developing a reliable vision system is a fundamental challenge for robotic technologies (e.g., indoor service robots and outdoor autonomous robots) which can ensure reliable navigation even in challenging environments such as adverse weather conditions (e.g., fog, rain), poor lighting conditions (e.g., over/under exposure), or sensor degradation (e.g., blurring, noise), and can guarantee high performance in safety-critical functions. Current solutions proposed to improve model robustness usually rely on generic data augmentation techniques or employ costly test-time adaptation methods. In addition, most approaches focus on addressing a single vision task (typically, image recognition) utilising synthetic data. In this paper, we introduce Per-corruption Adaptation of Normalization statistics (PAN) to enhance the model robustness of vision systems. Our approach entails three key components: (i) a corruption type identification module, (ii) dynamic adjustment of normalization layer statistics based on identified corruption type, and (iii) real-time update of these statistics according to input data. PAN can integrate seamlessly with any convolutional model for enhanced accuracy in several robot vision tasks. In our experiments, PAN obtains robust performance improvement on challenging real-world corrupted image datasets (e.g., OpenLoris, ExDark, ACDC), where most of the current solutions tend to fail. Moreover, PAN outperforms the baseline models by 20-30% on synthetic benchmarks in object recognition tasks.
Barbato, Francesco; Camuffo, Elena; Milani, Simone; Zanuttigh, Pietro
Continual Road-Scene Semantic Segmentation via Feature-Aligned Symmetric Multi-Modal Network Proceedings Article
In: IEEE International Conference on Image Processing (ICIP), 2024.
@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}
}
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.
Jung, HyunJun; Wu, Shun-Cheng; Ruhkamp, Patrick; Schieber, Hannah; Wang, Pengyuan; Rizzoli, Giulia; Zhao, Hongcheng; Meier, Sven Damian; Roth, Daniel; Navab, Nassir; others,
HouseCat6D-A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios Proceedings Article
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22498–22508, 2024, (Highlight).
@inproceedings{jung2024housecat6d,
title = {HouseCat6D-A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios},
author = {HyunJun Jung and Shun-Cheng Wu and Patrick Ruhkamp and Hannah Schieber and Pengyuan Wang and Giulia Rizzoli and Hongcheng Zhao and Sven Damian Meier and Daniel Roth and Nassir Navab and others},
year = {2024},
date = {2024-06-19},
urldate = {2024-06-19},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages = {22498--22508},
note = {Highlight},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Toldo, Marco; Michieli, Umberto; Zanuttigh, Pietro
Learning with Style: Continual Semantic Segmentation Across Tasks and Domains Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
@article{Toldo2022,
title = {Learning with Style: Continual Semantic Segmentation Across Tasks and Domains},
author = {Marco Toldo and Umberto Michieli and Pietro Zanuttigh},
url = {https://ieeexplore.ieee.org/document/10521870},
year = {2024},
date = {2024-05-07},
urldate = {2024-05-07},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Amann, Simon; Gatto, Alexander; Mel, Mazen
APPARATUSES AND METHODS FOR COMPUTER TOMOGRAPHY IMAGING SPECTROMETRY Patent
2024.
@patent{AMANN2024,
title = {APPARATUSES AND METHODS FOR COMPUTER TOMOGRAPHY IMAGING SPECTROMETRY},
author = {Simon Amann and Alexander Gatto and Mazen Mel},
year = {2024},
date = {2024-04-25},
urldate = {2024-04-25},
issue = {WO2024083580A1},
keywords = {},
pubstate = {published},
tppubtype = {patent}
}
Camuffo, Elena; Michieli, Umberto; Moon, Ji Joong; Kim, Daehyun; Ozay, Mete
FFT-based Selection And Optimization Of Statistics For Robust Recognition Of Severely Corrupted Images Proceedings Article
In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024.
@inproceedings{Camuffo2023d,
title = {FFT-based Selection And Optimization Of Statistics For Robust Recognition Of Severely Corrupted Images},
author = {Elena Camuffo and Umberto Michieli and Ji Joong Moon and Daehyun Kim and Mete Ozay},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10447639},
year = {2024},
date = {2024-01-26},
urldate = {2024-01-26},
booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
abstract = {Improving model robustness in case of corrupted images is among the key challenges to enable robust vision systems on smart devices, such as robotic agents. Particularly, robust test-time performance is imperative for most of the applications. This paper presents a novel approach to improve robustness of any classification model, especially on severely corrupted images. Our method (FROST) employs high-frequency features to detect input image corruption type, and select layer-wise feature normalization statistics. FROST provides the state-of-the-art results for different models and datasets, outperforming competitors on ImageNet-C by up to 37.1% relative gain, improving baseline of 40.9% mCE on severe corruptions.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Improving model robustness in case of corrupted images is among the key challenges to enable robust vision systems on smart devices, such as robotic agents. Particularly, robust test-time performance is imperative for most of the applications. This paper presents a novel approach to improve robustness of any classification model, especially on severely corrupted images. Our method (FROST) employs high-frequency features to detect input image corruption type, and select layer-wise feature normalization statistics. FROST provides the state-of-the-art results for different models and datasets, outperforming competitors on ImageNet-C by up to 37.1% relative gain, improving baseline of 40.9% mCE on severe corruptions.
Rizzoli, Giulia; Shenaj, Donald; Zanuttigh, Pietro
Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers Proceedings Article
In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 615–624, 2024.
@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}
}
Ferrarotti, Anna; Baldoni, Sara; Carli, Marco; Battisti, Federica
Stress Assessment for Augmented Reality Applications Based on Head Movement Features Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, pp. 1-14, 2024.
@article{10493844,
title = {Stress Assessment for Augmented Reality Applications Based on Head Movement Features},
author = {Anna Ferrarotti and Sara Baldoni and Marco Carli and Federica Battisti},
doi = {10.1109/TVCG.2024.3385637},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
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Barbato, Francesco; Michieli, Umberto; Yucel, Mehmet Kerim; Zanuttigh, Pietro; Ozay, Mete
A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation Proceedings Article
In: Proceedings of the 15th ACM Multimedia Systems Conference, pp. 190–201, Association for Computing Machinery, , Bari, Italy,, 2024, ISBN: 9798400704123.
@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},
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}
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.
Martin-Turrero, Carmen; Bouvier, Maxence; Breitenstein, Manuel; Zanuttigh, Pietro; Parret, Vincent
ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data Proceedings Article
In: Proceedings of the International Conference on Machine Learning (ICML), 2024.
@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}
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Baldoni, Sara; Battisti, Federica; Chiariotti, Federico; Mistrorigo, Fabio; Shofi, Alfi Baqiatus; Testolina, Paolo; Traspadini, Alessandro; Zanella, Andrea; Zorzi, Michele
Questset: A VR Dataset for Network and Quality of Experience Studies Proceedings Article
In: Proceedings of the 15th ACM Multimedia Systems Conference, pp. 408–414, 2024.
@inproceedings{baldoni2024questset,
title = {Questset: A VR Dataset for Network and Quality of Experience Studies},
author = {Sara Baldoni and Federica Battisti and Federico Chiariotti and Fabio Mistrorigo and Alfi Baqiatus Shofi and Paolo Testolina and Alessandro Traspadini and Andrea Zanella and Michele Zorzi},
url = {https://dl.acm.org/doi/abs/10.1145/3625468.3652187},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference},
pages = {408–414},
keywords = {},
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Barbato, Francesco; Michieli, Umberto; Toldo, Marco; Zanuttigh, Pietro
Road scenes segmentation across different domains by disentangling latent representations Journal Article
In: The Visual Computer, vol. 40, no. 2, pp. 811-830, 2024.
@article{Barbato2021b,
title = {Road scenes segmentation across different domains by disentangling latent representations},
author = {Francesco Barbato and Umberto Michieli and Marco Toldo and Pietro Zanuttigh},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {The Visual Computer},
volume = {40},
number = {2},
pages = {811-830},
keywords = {},
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}
Liu, Chang; Rizzoli, Giulia; Zanuttigh, Pietro; Li, Fu; Niu, Yi
Learning from the Web: Language Drives Weakly-Supervised Incremental Learning for Semantic Segmentation Proceedings Article
In: European Conference on Computer Vision, Springer 2024.
@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},
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Ferrarotti, Anna; Baldoni, Sara; Carli, Marco; Battisti, Federica
On the identification of the leading sensory cue in mulsemedia VR applications Proceedings Article
In: 2024 16th International Conference on Quality of Multimedia Experience (QoMEX), pp. 36-42, 2024.
@inproceedings{10598293,
title = {On the identification of the leading sensory cue in mulsemedia VR applications},
author = {Anna Ferrarotti and Sara Baldoni and Marco Carli and Federica Battisti},
doi = {10.1109/QoMEX61742.2024.10598293},
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Ferrarotti, Anna; Baldoni, Sara; Carli, Marco; Battisti, Federica
Interaction goes virtual: towards collaborative XR Proceedings Article
In: Proceedings of the 2024 ACM International Conference on Interactive Media Experiences, pp. 443–446, Association for Computing Machinery, Stockholm, Sweden, 2024, ISBN: 9798400705038.
@inproceedings{10.1145/3639701.3661089,
title = {Interaction goes virtual: towards collaborative XR},
author = {Anna Ferrarotti and Sara Baldoni and Marco Carli and Federica Battisti},
url = {https://doi.org/10.1145/3639701.3661089},
doi = {10.1145/3639701.3661089},
isbn = {9798400705038},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Proceedings of the 2024 ACM International Conference on Interactive Media Experiences},
pages = {443–446},
publisher = {Association for Computing Machinery},
address = {Stockholm, Sweden},
series = {IMX '24},
abstract = {This demo presents an interactive communication system based on immersive media for analyzing the users’ Quality of Experience in collaborative tasks. The interaction between users is studied in an asymmetric scenario where a peer-to-peer communication has been set up between a PC and a Virtual Reality headset. Two application scenarios have been considered: a Block Building task and a Treasure Hunt game. The two users will cooperate to perform the two tasks. The goal is to study the relation between the type of transmitted information (i.e., audio and video or audio only) and the quality and quantity of interaction. During the demo, participants will have the opportunity to try one of the designed applications.},
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This demo presents an interactive communication system based on immersive media for analyzing the users’ Quality of Experience in collaborative tasks. The interaction between users is studied in an asymmetric scenario where a peer-to-peer communication has been set up between a PC and a Virtual Reality headset. Two application scenarios have been considered: a Block Building task and a Treasure Hunt game. The two users will cooperate to perform the two tasks. The goal is to study the relation between the type of transmitted information (i.e., audio and video or audio only) and the quality and quantity of interaction. During the demo, participants will have the opportunity to try one of the designed applications.
Vangelista, L; Calvagno, G
On the Channel Activity Detection in LoRaWAN networks Journal Article
In: IEEE Open Journal of the Communications Society, 2024.
@article{vangelista2024channel,
title = {On the Channel Activity Detection in LoRaWAN networks},
author = {L Vangelista and G Calvagno},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Open Journal of the Communications Society},
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Barbato, Francesco; Michieli, Umberto; Moon, Jijoong; Zanuttigh, Pietro; Ozay, Mete
Cross-Architecture Auxiliary Feature Space Translation for Efficient Few-Shot Personalized Object Detection Journal Article
In: IEEE/RSJ International Conference on Intelligent Robots and System (IROS) 2024, 2024.
@article{barbato2024cross,
title = {Cross-Architecture Auxiliary Feature Space Translation for Efficient Few-Shot Personalized Object Detection},
author = {Francesco Barbato and Umberto Michieli and Jijoong Moon and Pietro Zanuttigh and Mete Ozay},
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Baldoni, Sara
Quality of Experience for immersive media: from content creation to rendering Proceedings Article
In: 2024 IEEE Symposium on Computers and Communications (ISCC), pp. 1-6, 2024.
@inproceedings{10733604,
title = {Quality of Experience for immersive media: from content creation to rendering},
author = {Sara Baldoni},
doi = {10.1109/ISCC61673.2024.10733604},
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date = {2024-01-01},
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Gallina, Annalisa; Amirpour, Hadi; Baldoni, Sara; Valenzise, Giuseppe; Battisti, Federica
Characterizing the Geometric Complexity of G-PCC Compressed Point Clouds Proceedings Article
In: 2024 IEEE International Conference on Visual Communications and Image Processing (VCIP), pp. 1-5, 2024.
@inproceedings{10849794,
title = {Characterizing the Geometric Complexity of G-PCC Compressed Point Clouds},
author = {Annalisa Gallina and Hadi Amirpour and Sara Baldoni and Giuseppe Valenzise and Federica Battisti},
doi = {10.1109/VCIP63160.2024.10849794},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
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abstract = {Measuring the complexity of visual content is crucial in various applications, such as selecting sources to test processing algorithms, designing subjective studies, and efficiently determining the appropriate encoding parameters and bandwidth allocation for streaming. While spatial and temporal complexity measures exist for 2D videos, a geometric complexity measure for 3D content is still lacking. In this paper, we present the first study to characterize the geometric complexity of 3D point clouds. Inspired by existing complexity measures, we propose several compression-based definitions of geometric complexity derived from the rate-distortion curves obtained by compressing a dataset of point clouds using G-PCC. Additionally, we introduce density-based and geometry-based descriptors to predict complexity. Our initial results show that even simple density measures can accurately predict the geometric complexity of point clouds.},
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Measuring the complexity of visual content is crucial in various applications, such as selecting sources to test processing algorithms, designing subjective studies, and efficiently determining the appropriate encoding parameters and bandwidth allocation for streaming. While spatial and temporal complexity measures exist for 2D videos, a geometric complexity measure for 3D content is still lacking. In this paper, we present the first study to characterize the geometric complexity of 3D point clouds. Inspired by existing complexity measures, we propose several compression-based definitions of geometric complexity derived from the rate-distortion curves obtained by compressing a dataset of point clouds using G-PCC. Additionally, we introduce density-based and geometry-based descriptors to predict complexity. Our initial results show that even simple density measures can accurately predict the geometric complexity of point clouds.
2023
Lincetto, Federico; Agresti, Gianluca; Rossi, Mattia; Zanuttigh, Pietro
Exploiting Multiple Priors for Neural 3D Indoor Reconstruction Proceedings Article
In: 34th British Machine Vision Conference, 2023.
@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},
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Baldoni, Sara; Sassi, Mohamed Saifeddine Hadj; Carli, Marco; Battisti, Federica
Definition of guidelines for virtual reality application design based on visual attention Journal Article
In: Multimedia Tools and Applications, 2023.
@article{Baldoni_MTAP_2023,
title = {Definition of guidelines for virtual reality application design based on visual attention},
author = {Sara Baldoni and Mohamed Saifeddine Hadj Sassi and Marco Carli and Federica Battisti},
doi = {10.1007/s11042-023-17488-y},
year = {2023},
date = {2023-11-03},
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abstract = {In virtual reality applications, head-mounted displays allow users to explore virtual surroundings, thus creating a high sense of immersion. However, due to the novelty of the technology and the possibility of freely enjoying a $$360^circ $$virtual world, users can get distracted and divert their attention from the content of the application. In this work, we define a set of guidelines for the design of virtual reality applications for enhancing the users’ attention. To the best of our knowledge, this is one of the first attempts to provide general guidelines for virtual application design based on visual attention. More specifically, we analyze the different categories of factors that contribute to the user’s responsiveness and define a set of experiments for measuring the user’s promptness with respect to visual stimuli with different features and in the presence of audio/visual distractions. Experimental tests have been carried out with 36 volunteers. The users’ reaction time has been recorded and the performed analysis allowed the definition of a set of guidelines based on individual, operational, and technological factors for the design of virtual reality applications optimized in terms of user attention. In particular, statistical tests demonstrated that the presence of distractions leads to significantly different reaction times with respect to the case of no distractions, and that users belonging to different age intervals have significantly different behaviors. Moreover, the optimal placement of objects has been identified and the impact of cybersickness has been analyzed.},
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In virtual reality applications, head-mounted displays allow users to explore virtual surroundings, thus creating a high sense of immersion. However, due to the novelty of the technology and the possibility of freely enjoying a $$360^circ $$virtual world, users can get distracted and divert their attention from the content of the application. In this work, we define a set of guidelines for the design of virtual reality applications for enhancing the users’ attention. To the best of our knowledge, this is one of the first attempts to provide general guidelines for virtual application design based on visual attention. More specifically, we analyze the different categories of factors that contribute to the user’s responsiveness and define a set of experiments for measuring the user’s promptness with respect to visual stimuli with different features and in the presence of audio/visual distractions. Experimental tests have been carried out with 36 volunteers. The users’ reaction time has been recorded and the performed analysis allowed the definition of a set of guidelines based on individual, operational, and technological factors for the design of virtual reality applications optimized in terms of user attention. In particular, statistical tests demonstrated that the presence of distractions leads to significantly different reaction times with respect to the case of no distractions, and that users belonging to different age intervals have significantly different behaviors. Moreover, the optimal placement of objects has been identified and the impact of cybersickness has been analyzed.
Devid, Campagnolo*; Elena, Camuffo*; Umberto, Michieli; Paolo, Borin; Simone, Milani; Andrea, Giordano
Fully Automated Scan-to-BIM via Point Cloud Instance Segmentation Proceedings Article
In: International Conference on Image Processing (ICIP), IEEE 2023.
@inproceedings{Camuffo2023c,
title = {Fully Automated Scan-to-BIM via Point Cloud Instance Segmentation},
author = {Campagnolo* Devid and Camuffo* Elena and Michieli Umberto and Borin Paolo and Milani Simone and Giordano Andrea
},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10222064},
year = {2023},
date = {2023-09-13},
urldate = {2023-09-13},
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organization = {IEEE},
abstract = {Digital reconstruction through Building Information Models (BIM) is a valuable methodology for documenting and analyzing existing buildings. Its pipeline starts with geometric acquisition. (e.g., via photogrammetry or laser scanning) for accurate point cloud collection. However, the acquired data are noisy and unstructured, and the creation of a semanticallymeaningful BIM representation requires a huge computational effort, as well as expensive and time-consuming human annotations. In this paper, we propose a fully automated scan-to-BIM pipeline. The approach relies on: (i) our dataset (HePIC), acquired from two large buildings and annotated at a point-wise semantic level based on existent BIM models; (ii) a novel ad hoc deep network (BIM-Net++) for semantic segmentation, whose output is then processed to extract instance information necessary to recreate BIM objects; (iii) novel model pretraining and class re-weighting to eliminate the need for a large amount of labeled data and human intervention.},
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Digital reconstruction through Building Information Models (BIM) is a valuable methodology for documenting and analyzing existing buildings. Its pipeline starts with geometric acquisition. (e.g., via photogrammetry or laser scanning) for accurate point cloud collection. However, the acquired data are noisy and unstructured, and the creation of a semanticallymeaningful BIM representation requires a huge computational effort, as well as expensive and time-consuming human annotations. In this paper, we propose a fully automated scan-to-BIM pipeline. The approach relies on: (i) our dataset (HePIC), acquired from two large buildings and annotated at a point-wise semantic level based on existent BIM models; (ii) a novel ad hoc deep network (BIM-Net++) for semantic segmentation, whose output is then processed to extract instance information necessary to recreate BIM objects; (iii) novel model pretraining and class re-weighting to eliminate the need for a large amount of labeled data and human intervention.
Baldoni, Sara; Wahba, Mahmoud Z. A.; Carli, Marco; Battisti, Federica
A study on the impact of virtual reality on user attention Conference
2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE, 2023.
@conference{Baldoni_ICASSP23,
title = {A study on the impact of virtual reality on user attention},
author = {Sara Baldoni and Mahmoud Z. A. Wahba and Marco Carli and Federica Battisti},
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Mari, Daniele; Camuffo, Elena; Milani, Simone
CACTUS: Content-Aware Compression and Transmission Using Semantics for Automotive LiDAR Data Journal Article
In: Sensors, vol. 23, iss. 12, 2023.
@article{Mari,
title = {CACTUS: Content-Aware Compression and Transmission Using Semantics for Automotive LiDAR Data},
author = {Daniele Mari and Elena Camuffo and Simone Milani},
editor = {MDPI},
url = {https://www.mdpi.com/1424-8220/23/12/5611},
doi = {10.3390/s23125611},
year = {2023},
date = {2023-06-15},
urldate = {2023-06-15},
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issue = {12},
abstract = {Many recent cloud or edge computing strategies for automotive applications require transmitting huge amounts of Light Detection and Ranging (LiDAR) data from terminals to centralized processing units. As a matter of fact, the development of effective Point Cloud (PC) compression strategies that preserve semantic information, which is critical for scene understanding, proves to be crucial. Segmentation and compression have always been treated as two independent tasks; however, since not all the semantic classes are equally important for the end task, this information can be used to guide data transmission. In this paper, we propose Content-Aware Compression and Transmission Using Semantics (CACTUS), which is a coding framework that exploits semantic information to optimize the data transmission, partitioning the original point set into separate data streams. Experimental results show that differently from traditional strategies, the independent coding of semantically consistent point sets preserves class information. Additionally, whenever semantic information needs to be transmitted to the receiver, using the CACTUS strategy leads to gains in terms of compression efficiency, and more in general, it improves the speed and flexibility of the baseline codec used to compress the data.},
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Many recent cloud or edge computing strategies for automotive applications require transmitting huge amounts of Light Detection and Ranging (LiDAR) data from terminals to centralized processing units. As a matter of fact, the development of effective Point Cloud (PC) compression strategies that preserve semantic information, which is critical for scene understanding, proves to be crucial. Segmentation and compression have always been treated as two independent tasks; however, since not all the semantic classes are equally important for the end task, this information can be used to guide data transmission. In this paper, we propose Content-Aware Compression and Transmission Using Semantics (CACTUS), which is a coding framework that exploits semantic information to optimize the data transmission, partitioning the original point set into separate data streams. Experimental results show that differently from traditional strategies, the independent coding of semantically consistent point sets preserves class information. Additionally, whenever semantic information needs to be transmitted to the receiver, using the CACTUS strategy leads to gains in terms of compression efficiency, and more in general, it improves the speed and flexibility of the baseline codec used to compress the data.
Baldoni, Sara; Battisti, Federica; Carli, Marco; Neri, Alessandro
A context-based framework for enhancing GNSS performance and security Conference
IEEE/ION PLANS2023, 2023.
@conference{Baldoni_ION23,
title = {A context-based framework for enhancing GNSS performance and security},
author = {Sara Baldoni and Federica Battisti and Marco Carli and Alessandro Neri },
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date = {2023-04-24},
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Camuffo, Elena; Milani, Simone
Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse Data Proceedings Article
In: International Conference of Computer Vision and Pattern Recognition Workshops, 2023.
@inproceedings{Camuffo2023b,
title = {Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse Data},
author = {Elena Camuffo and Simone Milani},
url = {https://arxiv.org/abs/2304.03980},
doi = {https://doi.org/10.48550/arXiv.2304.03980},
year = {2023},
date = {2023-04-10},
urldate = {2023-04-10},
booktitle = {International Conference of Computer Vision and Pattern Recognition Workshops},
abstract = {During the last few years, continual learning (CL) strategies for image classification and segmentation have been widely investigated designing innovative solutions to tackle catastrophic forgetting, like knowledge distillation and self-inpainting. However, the application of continual learning paradigms to point clouds is still unexplored and investigation is required, especially using architectures that capture the sparsity and uneven distribution of LiDAR data. The current paper analyzes the problem of class incremental learning applied to point cloud semantic segmentation, comparing approaches and state-of-the-art architectures. To the best of our knowledge, this is the first example of class-incremental continual learning for LiDAR point cloud semantic segmentation. Different CL strategies were adapted to LiDAR point clouds and tested, tackling both classic fine-tuning scenarios and the Coarse-to-Fine learning paradigm. The framework has been evaluated through two different architectures on SemanticKITTI, obtaining results in line with state-of-the-art CL strategies and standard offline learning.},
keywords = {},
pubstate = {published},
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During the last few years, continual learning (CL) strategies for image classification and segmentation have been widely investigated designing innovative solutions to tackle catastrophic forgetting, like knowledge distillation and self-inpainting. However, the application of continual learning paradigms to point clouds is still unexplored and investigation is required, especially using architectures that capture the sparsity and uneven distribution of LiDAR data. The current paper analyzes the problem of class incremental learning applied to point cloud semantic segmentation, comparing approaches and state-of-the-art architectures. To the best of our knowledge, this is the first example of class-incremental continual learning for LiDAR point cloud semantic segmentation. Different CL strategies were adapted to LiDAR point clouds and tested, tackling both classic fine-tuning scenarios and the Coarse-to-Fine learning paradigm. The framework has been evaluated through two different architectures on SemanticKITTI, obtaining results in line with state-of-the-art CL strategies and standard offline learning.
Lamichhane, Kamal; Neri, Michael; Battisti, Federica; Paudyal, Pradip; Carli, Marco
No-Reference Light Field Image Quality Assessment Exploiting Saliency Journal Article
In: IEEE Transactions on Broadcasting, pp. 1-11, 2023, ISSN: 0018-9316.
@article{nokey,
title = {No-Reference Light Field Image Quality Assessment Exploiting Saliency},
author = {Kamal Lamichhane and Michael Neri and Federica Battisti and Pradip Paudyal and Marco Carli},
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doi = {10.1109/TBC.2023.3242150},
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Siddiqui, Muhammad; Mel, Mazen
CAMERA, METHOD AND IMAGE PROCESSING METHOD Patent
2023.
@patent{nokey,
title = {CAMERA, METHOD AND IMAGE PROCESSING METHOD},
author = {Muhammad Siddiqui and Mazen Mel},
year = {2023},
date = {2023-01-26},
urldate = {2023-01-26},
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Baldoni, Sara; Poci, Ortisa; Calvagno, Giancarlo; Battisti, Federica
An Ablation Study on 360-Degree Saliency Estimation Proceedings Article
In: 2023 International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 1-6, 2023.
@inproceedings{Baldoni_ISPA_2023,
title = {An Ablation Study on 360-Degree Saliency Estimation},
author = {Sara Baldoni and Ortisa Poci and Giancarlo Calvagno and Federica Battisti},
url = {https://ieeexplore.ieee.org/abstract/document/10279300},
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Liu, Chang; Rizzoli, Giulia; Barbato, Francesco; Michieli, Umberto; Niu, Yi; Zanuttigh, Pietro
RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation Journal Article
In: arXiv preprint arXiv:2309.10479, 2023.
@article{liu2023recall+,
title = {RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation},
author = {Chang Liu and Giulia Rizzoli and Francesco Barbato and Umberto Michieli and Yi Niu and Pietro Zanuttigh},
year = {2023},
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Testolina, Paolo; Barbato, Francesco; Michieli, Umberto; Giordani, Marco; Zanuttigh, Pietro; Zorzi, Michele
SELMA: SEmantic Large-Scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints Journal Article
In: IEEE Transactions on Intelligent Transportation Systems, pp. 1–13, 2023.
@article{Testolina2023,
title = {SELMA: SEmantic Large-Scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints},
author = {Paolo Testolina and Francesco Barbato and Umberto Michieli and Marco Giordani and Pietro Zanuttigh and Michele Zorzi},
doi = {10.1109/tits.2023.3257086},
year = {2023},
date = {2023-01-01},
journal = {IEEE Transactions on Intelligent Transportation Systems},
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Coskun, Sahin; Yilmaz, Gokce Nur; Battisti, Federica; Alhussein, Musaed; Islam, Saiful
Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues Journal Article
In: Journal of Imaging, vol. 9, no. 12, pp. 281, 2023.
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Baldoni, Sara; Poci, Ortisa; Calvagno, Giancarlo; Battisti, Federica
An ablation study on 360-degree saliency estimation Proceedings Article
In: 2023 International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 1–6, IEEE 2023.
@inproceedings{baldoni2023ablation,
title = {An ablation study on 360-degree saliency estimation},
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Jesus, Gutiérrez; Dandyyeva, Gulzhanat; Magro, Matteo Dal; Cortés, Carlos; Brizzi, Michele; Carli, Marco; Battisti, Federica; others,
Subjective evaluation of dynamic point clouds: impact of compression and exploration behaviour Proceedings Article
In: Proceedings of the 31st European Signal Processing Conference, 2023.
@inproceedings{jesus2023subjective,
title = {Subjective evaluation of dynamic point clouds: impact of compression and exploration behaviour},
author = {Gutiérrez Jesus and Gulzhanat Dandyyeva and Matteo Dal Magro and Carlos Cortés and Michele Brizzi and Marco Carli and Federica Battisti and others},
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Coskun, Sahin; Yilmaz, Gokce Nur; Battisti, Federica; Alhussein, Musaed; Islam, Saiful
Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues Journal Article
In: Journal of Imaging, vol. 9, no. 12, pp. 281, 2023.
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Chao, Fang-Yi; Battisti, Federica; Lebreton, Pierre; Raake, Alexander
Omnidirectional video saliency Book Section
In: Immersive Video Technologies, pp. 123–158, Academic Press, 2023.
@incollection{Chao2023,
title = {Omnidirectional video saliency},
author = {Fang-Yi Chao and Federica Battisti and Pierre Lebreton and Alexander Raake},
year = {2023},
date = {2023-01-01},
booktitle = {Immersive Video Technologies},
pages = {123--158},
publisher = {Academic Press},
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Brizzi, Michele; Battisti, Federica; Carli, Marco; Neri, Alessandro
Selective video enhancement in the Laguerre–Gauss domain Journal Article
In: Signal Processing: Image Communication, vol. 110, pp. 116876, 2023.
@article{Brizzi2023,
title = {Selective video enhancement in the Laguerre--Gauss domain},
author = {Michele Brizzi and Federica Battisti and Marco Carli and Alessandro Neri},
year = {2023},
date = {2023-01-01},
journal = {Signal Processing: Image Communication},
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Lamichhane, Kamal; Carli, Marco; Battisti, Federica
A CNN-based no reference image quality metric exploiting content saliency Journal Article
In: Signal Processing: Image Communication, vol. 111, pp. 116899, 2023.
@article{Lamichhane2023,
title = {A CNN-based no reference image quality metric exploiting content saliency},
author = {Kamal Lamichhane and Marco Carli and Federica Battisti},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Signal Processing: Image Communication},
volume = {111},
pages = {116899},
publisher = {Elsevier},
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Barbato, Francesco; Rizzoli, Giulia; Zanuttigh, Pietro
DepthFormer: Multimodal Positional Encodings and Cross-Input Attention for Transformer-Based Segmentation Networks Proceedings Article
In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.
@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)},
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Shenaj, Donald; Toldo, Marco; Rigon, Alberto; Zanuttigh, Pietro
Asynchronous Federated Continual Learning Proceedings Article
In: CVPR FedVision Workshop, 2023.
@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},
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Amann, Simon; Mel, Mazen; Zanuttigh, Pietro; Haist, Tobias; Kamm, Markus; Gatto, Alexander; others,
Material Characterization using a Compact Computed Tomography Imaging Spectrometer with Super-resolution Capability Proceedings Article
In: Proceedings of the 6th International Conference on Optical Characterization of Materials, OCM 2023, pp. 139–148, 2023.
@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},
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Camuffo, Elena; Michieli, Umberto; Milani, Simone
Learning from Mistakes: Self-Regularizing Hierarchical Representations in Point Cloud Semantic Segmentation Journal Article
In: IEEE Transactions on Multimedia, pp. 1-11, 2023.
@article{Camuffo2023,
title = {Learning from Mistakes: Self-Regularizing Hierarchical Representations in Point Cloud Semantic Segmentation},
author = {Elena Camuffo and Umberto Michieli and Simone Milani},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10368362},
doi = {10.1109/TMM.2023.3345152},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {IEEE Transactions on Multimedia},
pages = {1-11},
abstract = {Recent advances in autonomous robotic technologies have highlighted the growing need for precise environmental analysis. LiDAR semantic segmentation has gained attention to accomplish fine-grained scene understanding by acting directly on raw content provided by sensors. Recent solutions showed how different learning techniques can be used to improve the performance of the model, without any architectural or dataset change. Following this trend, we present a coarse-to-fine setup that LEArns from classification mistaKes (LEAK) derived from a standard model. First, classes are clustered into macro groups according to mutual prediction errors; then, the learning process is regularized by: (1) aligning class-conditional prototypical feature representation for both fine and coarse classes, (2) weighting instances with a per-class fairness index. Our LEAK approach is very general and can be seamlessly applied on top of any segmentation architecture; indeed, experimental results showed that it enables state-of-the-art performances on different architectures, datasets and tasks, while ensuring more balanced class-wise results and faster convergence.},
keywords = {},
pubstate = {published},
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Recent advances in autonomous robotic technologies have highlighted the growing need for precise environmental analysis. LiDAR semantic segmentation has gained attention to accomplish fine-grained scene understanding by acting directly on raw content provided by sensors. Recent solutions showed how different learning techniques can be used to improve the performance of the model, without any architectural or dataset change. Following this trend, we present a coarse-to-fine setup that LEArns from classification mistaKes (LEAK) derived from a standard model. First, classes are clustered into macro groups according to mutual prediction errors; then, the learning process is regularized by: (1) aligning class-conditional prototypical feature representation for both fine and coarse classes, (2) weighting instances with a per-class fairness index. Our LEAK approach is very general and can be seamlessly applied on top of any segmentation architecture; indeed, experimental results showed that it enables state-of-the-art performances on different architectures, datasets and tasks, while ensuring more balanced class-wise results and faster convergence.