Silvia Zottin

Orcid: 0000-0003-0820-7260

According to our database1, Silvia Zottin authored at least 11 papers between 2022 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2025
In-domain versus out-of-domain transfer learning for document layout analysis.
Int. J. Document Anal. Recognit., June, 2025

FL-W3S: Cross-domain federated learning for weakly supervised semantic segmentation of white blood cells.
Int. J. Medical Informatics, 2025

Multi-Modal Analysis of Bi-Parametric MRI Slices for Lesion Detection in Prostate Cancer Screening.
IEEE Access, 2025

Exploring Few-Shot Text Line Segmentation Approaches in Challenging Ancient Manuscripts.
Proceedings of the 21st Conference on Information and Research science Connecting to Digital and Library science, 2025

2024
U-DIADS-Bib: a full and few-shot pixel-precise dataset for document layout analysis of ancient manuscripts.
Neural Comput. Appl., July, 2024

A One-Shot Learning Approach to Document Layout Segmentation of Ancient Arabic Manuscripts.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

ICDAR 2024 Competition on Few-Shot and Many-Shot Layout Segmentation of Ancient Manuscripts (SAM).
Proceedings of the Document Analysis and Recognition - ICDAR 2024 - 18th International Conference, Athens, Greece, August 30, 2024

2023
Few-Shot Pixel-Precise Document Layout Segmentation via Dynamic Instance Generation and Local Thresholding.
Int. J. Neural Syst., October, 2023

Efficient few-shot learning for pixel-precise handwritten document layout analysis.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Is ImageNet Always the Best Option? An Overview on Transfer Learning Strategies for Document Layout Analysis.
Proceedings of the Image Analysis and Processing - ICIAP 2023 Workshops, 2023

2022
Dynamic Instance Generation for Few-Shot Handwritten Document Layout Segmentation (short paper).
Proceedings of the 1st Workshop on Artificial Intelligence for Cultural Heritage, 2022


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