Prefer-DAS integrates self-training, prompt-guided contrastive learning, local direct preference optimization (LPO), and unsupervised preference optimization (UPO) to achieve effective domain adaptive segmentation in electron microscopy using sparse prompts and local preferences.
Prompt-das: Annotation-efficient prompt learning for domain adaptive semantic segmentation of electron microscopy images
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
-
Prefer-DAS: Learning from Local Preferences and Sparse Prompts for Domain Adaptive Segmentation of Electron Microscopy
Prefer-DAS integrates self-training, prompt-guided contrastive learning, local direct preference optimization (LPO), and unsupervised preference optimization (UPO) to achieve effective domain adaptive segmentation in electron microscopy using sparse prompts and local preferences.