UGDA-Net raises Dice coefficient by 9.3% over baseline on a 432-image plant seedling dataset by combining uncertainty-guided attention, entropy-weighted loss, and deep supervision.
Semantic segmentation of agricultural images: A survey.Information Processing in Agriculture, 11(2):172–186
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Uncertainty-Guided Attention and Entropy-Weighted Loss for Precise Plant Seedling Segmentation
UGDA-Net raises Dice coefficient by 9.3% over baseline on a 432-image plant seedling dataset by combining uncertainty-guided attention, entropy-weighted loss, and deep supervision.