Simone Milani | Associate Professor

@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},

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 = {2023-01-01},

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{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},

tppubtype = {inproceedings}

}

@inproceedings{Mari2022a,

title = {Features Denoising for Learned Image Coding},

author = {Daniele Mari and Simone Milani},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 10th European Workshop on Visual Information Processing (EUVIP)},

pages = {1--6},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Cardaioli2022,

title = {Hand Me Your PIN! Inferring ATM PINs of Users Typing with a Covered Hand},

author = {Matteo Cardaioli and Stefano Cecconello and Mauro Conti and Simone Milani and Stjepan Picek and Eugen Saraci},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {31st USENIX Security Symposium (USENIX Security 22)},

pages = {1687--1704},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Mari2022,

title = {The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection},

author = {Daniele Mari and Federica Latora and Simone Milani},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 IEEE International Workshop on Information Forensics and Security (WIFS)},

pages = {1--6},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Eleuch2022a,

title = {A Comparative Evaluation of Well-known Feature Extractors for Multi-view Vehicle Tracking in VSNs},

author = {Salma Eleuch and Nadia Khouja and Fethi Tlili and Tomaso Erseghe and Simone Milani},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 IEEE Ninth International Conference on Communications and Networking (ComNet)},

pages = {1--6},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Camuffo2022a,

title = {Deep 3D Model Optimization for Immersive and Interactive Applications},

author = {Elena Camuffo and Federica Battisti and Francesco Pham and Simone Milani},

url = {https://ieeexplore.ieee.org/abstract/document/9922742},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 10th European Workshop on Visual Information Processing (EUVIP)},

pages = {1--6},

organization = {IEEE},

abstract = {The growing diffusion of immersive and interactive applications is posing new challenges in the multimedia processing chain. When dealing with AR and VR applications, the most relevant aspects to consider are the (1) quality of the visualized 3D objects and (2) the fluidity in the visualization in case the user is moving in the environment. In this framework, we propose a deep learning based approach that estimates the optimal model parameters to be used in relation to the viewer’s movement and the model characteristics and quality. The performed tests show the effectiveness of the proposed approach.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Milani2021b,

title = {Adae: Adversarial distributed source autoencoder for point cloud compression},

author = {Simone Milani},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {2021 IEEE International Conference on Image Processing (ICIP)},

pages = {3078--3082},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Mari2021,

title = {Looking Through Walls: Inferring Scenes from Video-Surveillance Encrypted Traffic},

author = {Daniele Mari and Samuele Giuliano Piazzetta and Sara Bordin and Luca Pajola and Sebastiano Verde and Simone Milani and Mauro Conti},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {2595--2599},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pretto2021,

title = {A workflow and novel digital filters for compensating speed and equalization errors on digitized audio open-reel tapes},

author = {Niccol`o Pretto and Nadir Dalla Pozza and Alberto Padoan and Anthony Chmiel and Kurt James Werner and Alessandra Micalizzi and Emery Schubert and Antonio Rod`a and Simone Milani and Sergio Canazza},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Proceedings of the 16th International Audio Mostly Conference},

pages = {224--231},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bonettini2021,

title = {On the use of Benford's law to detect GAN-generated images},

author = {Nicolo Bonettini and Paolo Bestagini and Simone Milani and Stefano Tubaro},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {2020 25th international conference on pattern recognition (ICPR)},

pages = {5495--5502},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rossi2021,

title = {Neural network for denoising and reading degraded license plates},

author = {Gianmaria Rossi and Marco Fontani and Simone Milani},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10--15, 2021, Proceedings, Part VI},

pages = {484--499},

organization = {Springer International Publishing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Milani2020a,

title = {A syndrome-based autoencoder for point cloud geometry compression},

author = {Simone Milani},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {2020 IEEE International Conference on Image Processing (ICIP)},

pages = {2686--2690},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Castelletto2020,

title = {Phylogenetic minimum spanning tree reconstruction using autoencoders},

author = {Riccardo Castelletto and Simone Milani and Paolo Bestagini},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {2817--2821},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Agresti2019b,

title = {Material identification using RF sensors and convolutional neural networks},

author = {Gianluca Agresti and Simone Milani},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {3662--3666},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Verde2019,

title = {Phylogenetic analysis of software using cache miss statistics},

author = {Sebastiano Verde and Simone Milani and Giancarlo Calvagno},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {2552--2556},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Capraro2019,

title = {Rendering-aware point cloud coding for mixed reality devices},

author = {Fabio Capraro and Simone Milani},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {2019 IEEE International Conference on Image Processing (ICIP)},

pages = {3706--3710},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Verde2018a,

title = {Phylogenetic analysis of multimedia codec software},

author = {Sebastiano Verde and Simone Milani and Giancarlo Calvagno},

year  = {2018},

date = {2018-01-01},

booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},

pages = {1427--1431},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Limuti2018,

title = {A transform coding strategy for voxelized dynamic point clouds},

author = {Simone Limuti and Enrico Polo and Simone Milani},

year  = {2018},

date = {2018-01-01},

booktitle = {2018 25th IEEE International Conference on Image Processing (ICIP)},

pages = {2954--2958},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Michieletto2018,

title = {Improving Consensus-Based Distributed Camera Calibration Via Edge Pruning and Graph Traversal Initialization},

author = {Giulia Michieletto and Simone Milani and Angelo Cenedese and Giacomo Baggio},

year  = {2018},

date = {2018-01-01},

booktitle = {2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {3166--3170},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Verde2018,

title = {Video codec forensics based on convolutional neural networks},

author = {Sebastiano Verde and Luca Bondi and Paolo Bestagini and Simone Milani and Giancarlo Calvagno and Stefano Tubaro},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {2018 25th IEEE International Conference on Image Processing (ICIP)},

pages = {530--534},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Milani2017a,

title = {Video phylogeny tree reconstruction using aging measures},

author = {Simone Milani and Paolo Bestagini and Stefano Tubaro},

year  = {2017},

date = {2017-01-01},

booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},

pages = {2181--2185},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Milani2017e,

title = {Improving 3D reconstruction tracks using denoised euclidean distance matrices},

author = {Simone Milani},

year  = {2017},

date = {2017-01-01},

booktitle = {2017 IEEE International Conference on Image Processing (ICIP)},

pages = {740--744},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Milani2017f,

title = {Fast point cloud compression via reversible cellular automata block transform},

author = {Simone Milani},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {2017 IEEE International Conference on Image Processing (ICIP)},

pages = {4013--4017},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lecci2017,

title = {3D reconstruction from web harvested images using a forensic quality metric},

author = {Mattia Lecci and Simone Milani},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {1997--2001},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Verde2017,

title = {Audio phylogenetic analysis using geometric transforms},

author = {Sebastiano Verde and Simone Milani and Paolo Bestagini and Stefano Tubaro},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {2017 IEEE Workshop on Information Forensics and Security (WIFS)},

pages = {1--6},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Milani2016a,

title = {A rate control algorithm for video coding in augmented reality applications},

author = {Simone Milani and Gianluca Agresti and Giancarlo Calvagno},

year  = {2016},

date = {2016-01-01},

booktitle = {2016 Picture Coding Symposium (PCS)},

pages = {1--5},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Calemme2016,

title = {Depth map coding with elastic contours and 3D surface prediction},

author = {Marco Calemme and Pietro Zanuttigh and Simone Milani and Marco Cagnazzo and Beatrice Pesquet-Popescu},

year  = {2016},

date = {2016-01-01},

booktitle = {2016 IEEE International Conference on Image Processing (ICIP)},

pages = {1106--1110},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}