Modern image classification is based upon directly predicting model classes via large discriminative networks, making it difficult to assess the intuitive visual ``features'' that may constitute a classification decision. At the same time, recent works in joint visual language models such as CLIP provide ways to specify natural language descriptions of image classes but typically focus on providing single descriptions for each class. In this work, we demonstrate that an alternative approach, arguably more akin to our understanding of multiple ``visual features'' per class, can also provide compelling performance in the robust few-shot learning setting. In particular, we automatically enumerate multiple visual descriptions of each class -- via a large language model (LLM) -- then use a vision-image model to translate these descriptions to a set of multiple visual features of each image; we finally use sparse logistic regression to select a relevant subset of these features to classify each image. This both provides an ``intuitive'' set of relevant features for each class, and in the few-shot learning setting, outperforms standard approaches such as linear probing. When combined with finetuning, we also show that the method is able to outperform existing state-of-the-art finetuning approaches on both in-distribution and out-of-distribution performance.

通过使用大型语言模型自动列举每个类别的多个视觉描述，并使用视觉-图像模型将这些描述转化为每个图像的多个视觉特征集，然后使用稀疏逻辑回归选择相关特征子集进行分类，该方法在直观表示相关特征和少样本学习任务中表现出色，并在基于微调的方法中在分布内外任务上超过了现有的最先进方法。

利用多个描述特征进行强韧少样本图像学习