cross-domain few-shot learning
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Benchmarks
Office-Home
Most implemented
Self-Supervision Can Be a Good Few-Shot Learner
Cross-domain Few-shot Learning with Task-specific Adapters
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
Papers
When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning
Language descriptions in source-free cross-domain few-shot learning (SF-CDFSL) are often selected according to zero-shot accuracy obtained with a frozen vision--language model. This paper asks whether that ranking remain…
cross-domain few-shot learningImproving CLIP Adaptation by Breaking Tail Alignment for Source-Free Cross-Domain Few-Shot Learning
Vision-Language Models (VLMs) such as CLIP demonstrate strong zero-shot generalization, but their performance significantly degrades in cross-domain scenarios with scarce target-domain training data (Cross-Domain Few-Sho…
cross-domain few-shot learningZero-shot GeneralizationAddressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning
Vision-language models (VLMs) like CLIP have shown impressive generalization capabilities, yet their potential for Cross-Domain Few-Shot Learning (CDFSL) remains underexplored, where the model needs to transfer source-do…
cross-domain few-shot learningDomain AdaptationReviving In-domain Fine-tuning Methods for Source-Free Cross-domain Few-shot Learning
Cross-Domain Few-Shot Learning (CDFSL) aims to adapt large-scale pretrained models to specialized target domains with limited samples, yet the few-shot fine-tuning of vision-language models like CLIP remains underexplore…
cross-domain few-shot learningInterpretable Cross-Domain Few-Shot Learning with Rectified Target-Domain Local Alignment
Cross-Domain Few-Shot Learning (CDFSL) adapts models trained with large-scale general data (source domain) to downstream target domains with only scarce training data, where the research on vision-language models (e.g., …
cross-domain few-shot learningMedical DiagnosisMind the Discriminability Trap in Source-Free Cross-domain Few-shot Learning
Source-Free Cross-Domain Few-Shot Learning (SF-CDFSL) focuses on fine-tuning with limited training data from target domains (e.g., medical or satellite images), where Vision-Language Models (VLMs) such as CLIP and SigLIP…
cross-domain few-shot learning