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Prof. Dr. Gerard de Melo

Marco Cipriano

Chair of Artificial Intelligence and Intelligent Systems
Hasso Plattner Institute

Office: G-3.E.09
Tel.: +49 0331 5509-3469
Email: Marco.Cipriano(at)hpi.de
Links: Scholar

Supervisor: Prof. Dr. Gerard De Melo


Research Interests

Research Interests

My research focuses in general on improving the performances of Vision-and-Language models. In particular, I am currently focusing on Visual question answering (VQA), which is the multi-modal task of answering questions about given visual data. Due to my previous experience with medical imaging, I am specifically researching new ways to improve VQA models for the medical domain.
I am also interested in how state-of-the-art VL models handle specific cultural knowledge associated with image and text pairs.

> Vision-and-Language Models

> Visual Question Answering

> Computer Vision

> Medical Imaging


  1. Cipriano, M., Allegretti, S., Bolelli, F., Pollastri, F., & Grana, C. (2022). Improving segmentation of the inferior alveolar nerve through deep label propagation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  2. Cipriano, M., Allegretti, S., Bolelli, F., Di Bartolomeo, M., Pollastri, F., Pellacani, A., ... & Grana, C. (2022). Deep segmentation of the mandibular canal: a new 3d annotated dataset of CBCT volumes. IEEE Access.
  3. Pollastri, F., Cipriano, M., Bolelli, F., & Grana, C. (2022, March). Long-range 3d self-attention for mri prostate segmentation. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE.
  4. Mercadante, C., Cipriano, M., Bolelli, F., Pollastri, F., Anesi, A., & Grana, C. (2021). A cone beam computed tomography annotation tool for automatic detection of the inferior alveolar nerve canal. In 16th International Conference on Computer Vision Theory and Applications-VISAPP 2021. SciTePress.
  5. Vincenzi, S., Porrello, A., Buzzega, P., Cipriano, M., Fronte, P., Cuccu, R., ... & Calderara, S. (2021, January). The color out of space: learning self-supervised representations for earth observation imagery. In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE.