Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model

📰 ArXiv cs.AI

Cross-domain few-shot learning for hyperspectral image classification using a Mixup foundation model

advanced Published 8 Apr 2026

Action Steps

Utilize a Mixup foundation model to leverage few-shot learning capabilities
Apply cross-domain techniques to adapt the model to new, unseen domains
Fine-tune the model with a limited number of samples from the target domain
Evaluate the model's performance on the target domain using metrics such as accuracy and F1-score

Who Needs to Know This

ML researchers and engineers working on computer vision and hyperspectral image classification tasks can benefit from this approach to improve model performance and adaptability across different domains

Key Insight

💡 The proposed approach can effectively adapt to new domains with limited samples, reducing the need for extensive data augmentation and model updates

Key Takeaways

Cross-domain few-shot learning for hyperspectral image classification using a Mixup foundation model

Full Article

Title: Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model

Abstract:
arXiv:2601.22581v2 Announce Type: replace-cross Abstract: Although cross-domain few-shot learning (CDFSL) for hyper-spectral image (HSI) classification has attracted significant research interest, existing works often rely on an unrealistic data augmentation procedure in the form of external noise to enlarge the sample size, thus greatly simplifying the issue of data scarcity. They involve a large number of parameters for model updates, being prone to the overfitting problem. To the best of our

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