  Despite the notable advancements achieved by leveraging pre-trained
vision-language (VL) models through few-shot tuning for downstream tasks, our
detailed empirical study highlights a significant dependence of few-shot
learning outcomes on the careful selection of training examples - a facet that
has been previously overlooked in research. In this study, we delve into
devising more effective strategies for the meticulous selection of few-shot
training examples, as opposed to relying on random sampling, to enhance the
potential of existing few-shot prompt learning methodologies. To achieve this,
we assess the effectiveness of various Active Learning (AL) techniques for
instance selection, such as Entropy and Margin of Confidence, within the
context of few-shot training. Furthermore, we introduce two innovative
selection methods - Representativeness (REPRE) and Gaussian Monte Carlo
(Montecarlo) - designed to proactively pinpoint informative examples for
labeling in relation to pre-trained VL models. Our findings demonstrate that
both REPRE and Montecarlo significantly surpass both random selection and
AL-based strategies in few-shot training scenarios. The research also
underscores that these instance selection methods are model-agnostic, offering
a versatile enhancement to a wide array of few-shot training methodologies.
