Probabilistic Graphical Models Meet Deep Learning for Semantic Segmentation: Mathematical connections and recent developments
- DOI
- 10.1109/msp.2025.3648958
- Published
- 2026-03
- Container
- IEEE Signal Processing Magazine
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/msp.2025.3648958,
title = {Probabilistic Graphical Models Meet Deep Learning for Semantic Segmentation: Mathematical connections and recent developments},
author = {Martina Pastorino and Gabriele Moser and Sebastiano B. Serpico and Josiane Zerubia},
year = {2026},
journal = {IEEE Signal Processing Magazine},
doi = {10.1109/msp.2025.3648958},
url = {https://doi.org/10.1109/msp.2025.3648958}
}RIS
TY - JOUR TI - Probabilistic Graphical Models Meet Deep Learning for Semantic Segmentation: Mathematical connections and recent developments AU - Martina Pastorino AU - Gabriele Moser AU - Sebastiano B. Serpico AU - Josiane Zerubia PY - 2026 JO - IEEE Signal Processing Magazine DO - 10.1109/msp.2025.3648958 UR - https://doi.org/10.1109/msp.2025.3648958 ER -
APA
Pastorino, M., Moser, G., Serpico, S. B., & Zerubia, J. (2026). Probabilistic Graphical Models Meet Deep Learning for Semantic Segmentation: Mathematical connections and recent developments. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2025.3648958
Source records
- crossref · retrieved 2026-09-25T03:31:52.082Z