Modeling cross-scale consistency via dual-view learning for semi-supervised semantic segmentation.
- DOI
- 10.1016/j.neunet.2026.109547
- Published
- 2026 Aug 26
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109547,
title = {Modeling cross-scale consistency via dual-view learning for semi-supervised semantic segmentation.},
author = {Min C and Lei T and Wang X and Wang Y and Nandi AK},
year = {2026},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109547},
url = {https://doi.org/10.1016/j.neunet.2026.109547}
}RIS
TY - JOUR TI - Modeling cross-scale consistency via dual-view learning for semi-supervised semantic segmentation. AU - Min C AU - Lei T AU - Wang X AU - Wang Y AU - Nandi AK PY - 2026 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109547 UR - https://doi.org/10.1016/j.neunet.2026.109547 ER -
APA
C, M., T, L., X, W., Y, W., & AK, N. (2026). Modeling cross-scale consistency via dual-view learning for semi-supervised semantic segmentation.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109547
Source records
- pubmed · retrieved 2026-09-26T05:27:24.897Z