Phone (office): +39 049 827 7774
Biography
Pietro Zanuttigh was born in 1978. He graduated in Computer Engineering at the University of Padova in 2003 and got the PhD from same university in 2007. He has been an assistant professor at the University of Padova from 2007 to 2020. From 2020 he is an associate professor at the same university. He has also been a visiting researcher at the University of New South Wales (Sydney, Australia) in 2006 and in 2010. He works in the multimedia research group and his research activity is focused on image and 3D data processing and understanding.
His research interest includes semantic scene understanding with a special focus on domain adaptation and incremental learning techniques for deep networks and multi-modal data, federated learning techniques for computer vision, 3D data acquisition, Time-of-Flight sensor data processing, gesture recognition, 3D shape classification and the compression of depth maps and multi-view representations. He is an associate editor of Springer’s Visual Comptuter and an editorial board member of MDPI Technologies. He has been involved in several European (VITRA, MUSTEEL, Mu-Blast), National and university funded projects. He has also been the scientific coordinator for industry funded projects including the “Next generation Time-of-Flight cameras” project funded by Sony EuTec. He has co-authored two books and many journal papers and conference proceedings.
Research areas
Publications
2024
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.
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.
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.
2023
Exploiting Multiple Priors for Neural 3D Indoor Reconstruction Proceedings Article
In: 34th British Machine Vision Conference, 2023.
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning Proceedings Article
In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023, 2023.
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.
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.
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.
Asynchronous Federated Continual Learning Proceedings Article
In: CVPR FedVision Workshop, 2023.
SynDrone-Multi-Modal UAV Dataset for Urban Scenarios Proceedings Article
In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2210–2220, 2023.
Federated Learning in Computer Vision Journal Article
In: IEEE Access, 2023.
RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation Journal Article
In: arXiv preprint arXiv:2309.10479, 2023.
Continual Road-Scene Semantic Segmentation via Feature-Aligned Symmetric Multi-Modal Network Journal Article
In: arXiv preprint arXiv:2308.04702, 2023.
2022
Multimodal Semantic Segmentation in Autonomous Driving: A Review of Current Approaches and Future Perspectives Journal Article
In: Technologies, vol. 10, no. 4, pp. 90, 2022.
Continual coarse-to-fine domain adaptation in semantic segmentation Journal Article
In: Image and Vision Computing, vol. 121, pp. 104426, 2022.
Time-of-flight simulation data training circuitry, time-of-flight simulation data training method, time-of-flight simulation data output method, time-of-flight simulation data output circuitry Patent
2022, (US Patent App. 17/527,191).
Lightweight Deep Learning Architecture for MPI Correction and Transient Reconstruction Journal Article
In: IEEE Transactions on Computational Imaging, vol. 8, pp. 721–732, 2022.
Domain adaptation and continual learning in semantic segmentation Book Section
In: Advanced Methods and Deep Learning in Computer Vision, pp. 275–303, Academic Press, 2022.
End-to-end learning for joint depth and image reconstruction from diffracted rotation Journal Article
In: arXiv preprint arXiv:2204.07076, 2022.
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps Proceedings Article
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2687–2696, 2022.
Reframing control methods for parameters optimization in adversarial image generation Journal Article
In: Neural Networks, vol. 153, pp. 303–313, 2022.
Edge-Aware Graph Matching Network for Part-Based Semantic Segmentation Journal Article
In: International Journal of Computer Vision, vol. 130, no. 11, pp. 2797–2821, 2022.
Learning with Style: Continual Semantic Segmentation Across Tasks and Domains Journal Article
In: arXiv preprint arXiv:2210.07016, 2022.
End-to-end learning for joint depth and image reconstruction from diffracted rotation Journal Article
In: arXiv preprint arXiv:2204.07076, 2022.
Joint Reconstruction and Super Resolution of Hyper-Spectral CTIS Images Proceedings Article
In: British Machine Vision Conference (BMVC), 2022.
Joint Reconstruction and Super Resolution of Hyper-Spectral CTIS Images Conference
BMVC, 2022.
Time-of-flight simulation data training circuitry, time-of-flight simulation data training method, time-of-flight simulation data output method, time-of-flight simulation data output circuitry Patent
2022.
Unsupervised Network Anomaly Detection by Learning on 2D Data Representations Proceedings Article
In: 2022 9th Swiss Conference on Data Science (SDS), pp. 53–58, IEEE 2022.
2021
Apparatuses and methods for training a machine learning network for use with a time-of-flight camera Miscellaneous
2021, (US Patent App. 17/110,330).
Adapting Segmentation Networks to New Domains by Disentangling Latent Representations. Journal Article
In: CoRR, 2021.
Latent space regularization for unsupervised domain adaptation in semantic segmentation Proceedings Article
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2835–2845, 2021.
Road scenes segmentation across different domains by disentangling latent representations Journal Article
In: arXiv preprint arXiv:2108.03021, 2021.
Knowledge distillation for incremental learning in semantic segmentation Journal Article
In: Computer Vision and Image Understanding, vol. 205, pp. 103167, 2021.
Unsupervised domain adaptation with multiple domain discriminators and adaptive self-training Proceedings Article
In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 2845–2852, IEEE 2021.
Unsupervised domain adaptation in semantic segmentation via orthogonal and clustered embeddings Proceedings Article
In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1358–1368, 2021.
Continual semantic segmentation via repulsion-attraction of sparse and disentangled latent representations Proceedings Article
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1114–1124, 2021.
Deep learning for transient image reconstruction from ToF data Journal Article
In: Sensors, vol. 21, no. 6, pp. 1962, 2021.
Recall: Replay-based continual learning in semantic segmentation Proceedings Article
In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7026–7035, 2021.
Unsupervised domain adaptation of deep networks for ToF depth refinement Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 9195–9208, 2021.
Semi-supervised deep learning techniques for spectrum reconstruction Proceedings Article
In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 7767–7774, IEEE 2021.
2020
Adversarial learning and self-teaching techniques for domain adaptation in semantic segmentation Journal Article
In: IEEE Transactions on Intelligent Vehicles, vol. 5, no. 3, pp. 508–518, 2020.
Unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment Journal Article
In: Image and Vision Computing, vol. 95, pp. 103889, 2020.
Unsupervised domain adaptation in semantic segmentation: a review Journal Article
In: Technologies, vol. 8, no. 2, pp. 35, 2020.
Gmnet: Graph matching network for large scale part semantic segmentation in the wild Proceedings Article
In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VIII 16, pp. 397–414, Springer International Publishing 2020.
2019
Stereo and ToF data fusion by learning from synthetic data Journal Article
In: Information Fusion, vol. 49, pp. 161–173, 2019.
Unsupervised domain adaptation for tof data denoising with adversarial learning Proceedings Article
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5584–5593, 2019.
Unsupervised domain adaptation for semantic segmentation of urban scenes Proceedings Article
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0–0, 2019.
Incremental learning techniques for semantic segmentation Proceedings Article
In: Proceedings of the IEEE/CVF international conference on computer vision workshops, pp. 0–0, 2019.
Region Merging Driven by Deep Learning for RGB-D Segmentation and Labeling Proceedings Article
In: Proceedings of the 13th International Conference on Distributed Smart Cameras, pp. 1–6, 2019.
A multi-camera dataset for depth estimation in an indoor scenario Journal Article
In: Data in brief, vol. 27, pp. 104619, 2019.