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Phone (office): +39 049 827 7774
Biography
Francesco Barbato is a 2nd year Ph.D. Student in Information Engineering at the Department of Information Engineering of the University of Padua.
He received his Bachelor’s degree in Information Engineering in 2018 and his Master’s Degree (with honors) in ICT for Internet and Multimedia in 2020, both from the University of Padova. Afterward, he was awarded a research grant and spent a year in the LTTM laboratory as a collaborator before joining it as a Ph.D. student in 2021.
His research interests and activities regard Computer Vision and Deep Learning for scene understanding (Domain Adaptation, Continual Learning, and Multimodal Learning) with a particular focus on autonomous driving scenarios.
Research areas
Semantic Segmentation
Image Understanding task dealing with the dense labeling of input data, during my research I investigated both 2D (images), 3D (point clouds), and Multi-Modal settings.
Continual Learning
This setup implies the training in subsequent steps of a neural network, each time adding new classes to be recognized. I have investigated this technique in Semantic Segmentation.
Unsupervised Domain Adaptation
This task deals with the adaptation of a neural network to a new domain without requiring labeled samples from it. It has been widely investigated in Semantic Segmentation due to the prohibitive labeling costs.
Multi-Modal Learning
This setting investigates the possibility of using neural network with heterogeneous input data. Common examples are RGB-Depth or RGB-Thermal, recently there has been a lot of interest in the 2D-3D case.
Publications
2024
Continual Road-Scene Semantic Segmentation via Feature-Aligned Symmetric Multi-Modal Network Proceedings Article
In: IEEE International Conference on Image Processing (ICIP), 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.
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.
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.
RECALL+: Adversarial Web-based Replay for Continual Learning in Semantic Segmentation Journal Article
In: arXiv preprint arXiv:2309.10479, 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.
2021
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.