The current developments in the research area of few-shot learning (FSL) and domain adaptation are significantly advancing the field, particularly in addressing challenges related to open-set recognition and source-free adaptation. A notable trend is the integration of advanced machine learning techniques such as diffusion models and neural ordinary differential equations (ODE) to enhance the robustness and efficiency of FSL models. These techniques are being employed to optimize prototypes, refine classification boundaries, and improve the transferability of models across domains. Additionally, self-supervised learning is being leveraged to improve feature extraction and classification performance in FSL, demonstrating promising results in enhancing model accuracy and generalization under limited data conditions. The field is also witnessing innovative approaches to cold start problems in computerized adaptive testing, where prior knowledge is effectively utilized to mitigate initial data scarcity. Notably, the development of frameworks that can handle open-set domain adaptation without source data is proving to be a significant breakthrough, addressing privacy concerns and distribution shifts effectively. These advancements collectively push the boundaries of FSL and domain adaptation, making them more applicable and robust in real-world scenarios.