ReferEndoscopy plus attribute-retrieval and frequency-aware fusion yields open-vocabulary compositional referring segmentation that outperforms natural-image RIS baselines on endoscopic data and generalizes to an unseen robotic prostatectomy set.
Unet++: Redesigning skip connections to exploit multiscale features in image segmentation.IEEE transactions on medical imaging, 39(6):1856–1867, 2019
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Attribute Retrieving for Open-Vocabulary Endoscopic Compositional Referring Segmentation
ReferEndoscopy plus attribute-retrieval and frequency-aware fusion yields open-vocabulary compositional referring segmentation that outperforms natural-image RIS baselines on endoscopic data and generalizes to an unseen robotic prostatectomy set.