  Few-shot image classification is the task of classifying unseen images to one
of N mutually exclusive classes, using only a small number of training examples
for each class. The limited availability of these examples (denoted as K)
presents a significant challenge to classification accuracy in some cases. To
address this, we have developed a method for augmenting the set of K with an
addition set of A retrieved images. We call this system Retrieval-Augmented
Few-shot Image Classification (RAFIC). Through a series of experiments, we
demonstrate that RAFIC markedly improves performance of few-shot image
classification across two challenging datasets. RAFIC consists of two main
components: (a) a retrieval component which uses CLIP, LAION-5B, and faiss, in
order to efficiently retrieve images similar to the supplied images, and (b)
retrieval meta-learning, which learns to judiciously utilize the retrieved
images. Code and data is available at github.com/amirziai/rafic.
