A teacher-student framework that separates class and mask confidence thresholds, uses CLIP to correct category pseudo-labels, and reweights mask loss by per-pixel uncertainty sets new state-of-the-art semi-supervised instance segmentation results.
Deep watershed transform for instance segmentation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation
A teacher-student framework that separates class and mask confidence thresholds, uses CLIP to correct category pseudo-labels, and reweights mask loss by per-pixel uncertainty sets new state-of-the-art semi-supervised instance segmentation results.