The field of cross-domain few-shot learning is moving towards addressing the challenges of transferring knowledge from a source domain to target domains with limited annotations. Researchers are exploring new methods to improve the generalization and fine-tuning of models in this setting. A key area of focus is on mitigating the entanglement problem that arises when using Vision Transformers (ViTs) for cross-domain few-shot learning. Another important direction is the development of novel approaches to prompt tuning, which can improve the transferability of ViTs across domains. Notably, some papers have shown that disrupting the continuity of image tokens in ViTs can lead to improved performance in target domains. Overall, the field is witnessing significant innovations in methods and techniques to tackle the complexities of cross-domain few-shot learning. Noteworthy papers in this area include: Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation, which proposes a method to address the entanglement problem in ViTs. Random Registers for Cross-Domain Few-Shot Learning, which introduces a novel approach to prompt tuning using random registers. Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning, which explores the role of image tokens' continuity in ViT's generalization. Multiple Stochastic Prompt Tuning for Practical Cross-Domain Few Shot Learning, which proposes a framework for practical cross-domain few-shot learning using multiple stochastic prompts.