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.

翻译：基础视觉Transformer模型在众多视觉任务中展现出令人瞩目的少样本学习能力。本研究首次系统探索了在主动学习框架中应用参数高效微调方法，以推动极端预算约束分类任务中的样本选择过程优化。针对具有分布外特性的图像数据集的研究，为我们的工作增添了复杂性与现实意义。通过详尽评估，我们展示了在这些具有挑战性数据集上主动学习性能的提升，凸显了将参数高效微调方法与基础模型相结合的战略优势。这为优化主动学习策略的广泛讨论提供了新见解，并为未来在专业领域中利用基础模型实现高效数据标注开辟了富有前景的研究方向。