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 11–20 of 74 papers

TitleStatusHype
Exploring Cross-Domain Few-Shot Classification via Frequency-Aware PromptingCode0
Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot LearningCode0
Understanding the Cross-Domain Capabilities of Video-Based Few-Shot Action Recognition Models—0
Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition—0
A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (MedIMeta)—0
Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot LearningCode1
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot LearningCode1
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot LearningCode1
Cross-Domain Few-Shot Learning via Adaptive Transformer NetworksCode0
Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot LearningCode0
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