Foundational vision transformer models have shown impressive few shot
performance on many vision tasks. This research presents a novel investigation
into the application of parameter efficient fine-tuning methods within an
active learning (AL) framework, to advance the sampling selection process in
extremely budget constrained classification tasks. The focus on image datasets,
known for their out-of-distribution characteristics, adds a layer of complexity
and relevance to our study. Through a detailed evaluation, we illustrate the
improved AL performance on these challenging datasets, highlighting the
strategic advantage of merging parameter efficient fine tuning methods with
foundation models. This contributes to the broader discourse on optimizing AL
strategies, presenting a promising avenue for future exploration in leveraging
foundation models for efficient and effective data annotation in specialized
domains.

通过在活动学习（AL）框架内应用参数高效微调方法对极限预算限制的分类任务中的采样选择过程进行研究，本研究展示了基础视觉转换模型在图像数据集上表现出色的少样本性能，并突出显示了将参数高效微调方法与基础模型相结合的战略优势，从而改进了这些具有挑战性的数据集上的活动学习性能，为优化 AL 策略做出了贡献，并为在专业领域高效和有效地进行数据注释提供了有前景的研究方向。