Research

At CILab, our research focuses on designing innovative Computational Intelligence solutions that combine machine learning, deep learning, fuzzy modeling, and explainable AI to tackle real-world challenges. We aim to bridge theory and practice, developing models that are efficient, interpretable, and applicable across diverse domains such as healthcare, multimedia, autonomous systems, and so on.

Some recent publications

• Brescia, F., Alemán Santana, B.E., Diaz, M., Vessio, G., Ferrer, M.A., & Castellano, G. (2025). Integrating robotic kinematics and dynamics with online handwriting features for dysgraphia classification. Biomedical Signal Processing and Control.

• De Marinis, P., Fanelli, N., Scaringi, R., Colonna, E., Fiameni, G., Vessio, G., & Castellano, G. (2025). Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts. ECAI.

• Valerio, A.G., Trufanova, K., de Benedictis, S., Vessio, G., & Castellano, G. (2025). From segmentation to explanation: Generating textual reports from MRI with LLMs. Computer Methods and Programs in Biomedicine.

• Scaringi, R., Fiameni, G., Vessio, G., & Castellano, G. (2025). GraphCLIP: Image-graph contrastive learning for multimodal artwork classification. Knowledge-Based Systems.

• Fanelli, N., Vessio, G., & Castellano, G. (2025). I Dream My Painting: Connecting MLLMs and Diffusion Models via Prompt Generation for Text-Guided Multi-Mask Inpainting. WACV.

• Rinaldi, I., Fanelli, N., Castellano, G., & Vessio, G. (2024). Art2Mus: Bridging Visual Arts and Music through Cross-Modal Generation. ECCVW.

• Castellano, G., Esposito, A., Lella, E., Montanaro, G., & Vessio, G. (2024). Automated detection of Alzheimer's disease: a multi-modal approach with 3D MRI and amyloid PET. Scientific Reports. 

• Castellano, G., De Marinis, P., & Vessio, G. (2023). Weed mapping in multispectral drone imagery using lightweight vision transformers. Neurocomputing.

• Castellano, G., Cotardo, E., Mencar, C., & Vessio, G. (2023). Density-based clustering with fully-convolutional networks for crowd flow detection from drones. Neurocomputing.

• Castellano, G., & Vessio, G. (2022). A deep learning approach to clustering visual arts. International Journal of Computer Vision.

• Castellano, G., Digeno, V., Sansaro, G., & Vessio, G. (2022). Leveraging knowledge graphs and deep learning for automatic art analysis. Knowledge-Based Systems.

• Kaczmarek-Majer, K., Casalino, G., Castellano, G., Dominiak, M., Hryniewicz, O., Kamińska, O., ... & Díaz-Rodríguez, N. (2022). PLENARY: Explaining black-box models in natural language through fuzzy linguistic summaries. Information Sciences.

• Kaczmarek-Majer, K., Casalino, G., Castellano, G., Hryniewicz, O., & Dominiak, M. (2022). Explaining smartphone-based acoustic data in bipolar disorder: Semi-supervised fuzzy clustering and relative linguistic summaries. Information Sciences.

• Casalino, G., Castellano, G., Castiello, C., & Mencar, C. (2022). Effect of fuzziness in fuzzy rule-based classifiers defined by strong fuzzy partitions and winner-takes-all inference. Soft Computing.

• Castellano, G., & Vessio, G. (2021). Deep learning approaches to pattern extraction and recognition in paintings and drawings: An overview. Neural Computing and Applications.

• Castellano, G., Lella, E., & Vessio, G. (2021). Visual link retrieval and knowledge discovery in painting datasets. Multimedia Tools and Applications.

• Moral, A., Castiello, C., Magdalena, L., & Mencar, C. (2021). Explainable fuzzy systems. Springer International Publishing.

• Castellano, G., Castiello, C., Mencar, C., & Vessio, G. (2020). Crowd detection in aerial images using spatial graphs and fully-convolutional neural networks. IEEE Access.