  The pretraining-finetuning paradigm has gained widespread adoption in vision
tasks and other fields, yet it faces the significant challenge of high sample
annotation costs. To mitigate this, the concept of active finetuning has
emerged, aiming to select the most appropriate samples for model finetuning
within a limited budget. Traditional active learning methods often struggle in
this setting due to their inherent bias in batch selection. Furthermore, the
recent active finetuning approach has primarily concentrated on aligning the
distribution of selected subsets with the overall data pool, focusing solely on
diversity. In this paper, we propose a Bi-Level Active Finetuning framework to
select the samples for annotation in one shot, which includes two stages: core
sample selection for diversity, and boundary sample selection for uncertainty.
The process begins with the identification of pseudo-class centers, followed by
an innovative denoising method and an iterative strategy for boundary sample
selection in the high-dimensional feature space, all without relying on
ground-truth labels. Our comprehensive experiments provide both qualitative and
quantitative evidence of our method's efficacy, outperforming all the existing
baselines.
