All Outputs (5)

Cloud Detection Challenge - Methods and Results (2025)
Journal Article
Chisari, A. B., Guarnera, L., Ortis, A., Patatu, W. C., Casella, B., Naso, L., Puglisi, G., Del Zoppo, V., Giuffrida, M. V., & Battiato, S. (2025). Cloud Detection Challenge - Methods and Results. IEEE Access, https://doi.org/10.1109/ACCESS.2025.3553422

Accurate cloud detection is critical for advancing atmospheric monitoring and meteorological forecasting. This paper presents the Cloud Detection Challenge, an initiative aimed at enhancing cloud detection through innovative solutions using lidar-bas... Read More about Cloud Detection Challenge - Methods and Results.

Evaluating Language Model Vulnerability to Poisoning Attacks in Low-Resource Settings (2024)
Journal Article
Plant, R., Giuffrida, M. V., Pitropakis, N., & Gkatzia, D. (2025). Evaluating Language Model Vulnerability to Poisoning Attacks in Low-Resource Settings. IEEE Transactions on Audio, Speech and Language Processing, 33, 54-67. https://doi.org/10.1109/taslp.2024.3507565

Pre-trained language models are a highly effective source of knowledge transfer for natural language processing tasks, as their development represents an investment of resources beyond the reach of most researchers and end users. The widespread avail... Read More about Evaluating Language Model Vulnerability to Poisoning Attacks in Low-Resource Settings.

Synchronization Is All You Need: Exocentric-to-Egocentric Transfer for Temporal Action Segmentation with Unlabeled Synchronized Video Pairs (2024)
Presentation / Conference Contribution
Quattrocchi, C., Furnari, A., Di Mauro, D., Giuffrida, M. V., & Farinella, G. M. (2024, September). Synchronization Is All You Need: Exocentric-to-Egocentric Transfer for Temporal Action Segmentation with Unlabeled Synchronized Video Pairs. Presented at Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy

We consider the problem of transferring a temporal action segmentation system initially designed for exocentric (fixed) cameras to an egocentric scenario, where wearable cameras capture video data. The conventional supervised approach requires the co... Read More about Synchronization Is All You Need: Exocentric-to-Egocentric Transfer for Temporal Action Segmentation with Unlabeled Synchronized Video Pairs.

TADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression on Brain MRI (2024)
Presentation / Conference Contribution
Litrico, M., Guarnera, F., Giuffrida, M. V., Ravì, D., & Battiato, S. (2024, October). TADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression on Brain MRI. Presented at MICCAI 2024: 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, Marrakesh, Morocco

Generating realistic images to accurately predict changes in the structure of brain MRI can be a crucial tool for clinicians. Such applications can help assess patients’ outcomes and analyze how diseases progress at the individual level. However, exi... Read More about TADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression on Brain MRI.

Adapting Vision Foundation Models for Plant Phenotyping (2023)
Presentation / Conference Contribution
Chen, F., Giuffrida, M. V., & Tsaftaris, S. A. (2023, October). Adapting Vision Foundation Models for Plant Phenotyping. Presented at International Conference on Computer Vision (ICCV) Workshops, Paris, France

Foundation models are large models pre-trained on tremendous amount of data. They can be typically adapted to diverse downstream tasks with minimal effort. However, as foundation models are usually pre-trained on images or texts sourced from the Inte... Read More about Adapting Vision Foundation Models for Plant Phenotyping.