SeGD-VPT uses text-guided visual prompts to generate diverse features, reporting 58.31% (1-shot) and 66.76% (5-shot) average accuracy on four CD-FSL benchmarks.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting
SeGD-VPT uses text-guided visual prompts to generate diverse features, reporting 58.31% (1-shot) and 66.76% (5-shot) average accuracy on four CD-FSL benchmarks.