SOTAVerified

cross-domain few-shot learning

Its essence is transfer learning. The model needs to be trained in the source domain and then migrated to the target domain. Compliant with (1) the category in the target domain has never appeared in the source domain (2) the data distribution of the target domain is inconsistent with the source domain (3) each class in the target domain has very few labels

Papers

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Showing 1–10 of 74 papers

TitleStatusHype
FAD: Frequency Adaptation and Diversion for Cross-domain Few-shot Learning—0
A Cross-Domain Few-Shot Learning Method Based on Domain Knowledge Mapping—0
From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning—0
Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning—0
Task-Specific Preconditioner for Cross-Domain Few-Shot Learning—0
Enhancing Masked Time-Series Modeling via Dropping PatchesCode0
SVasP: Self-Versatility Adversarial Style Perturbation for Cross-Domain Few-Shot LearningCode0
Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting—0
Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot LearningCode1
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning—0
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