  Few-shot image classification aims to learn an image classifier using only a
small set of labeled examples per class. A recent research direction for
improving few-shot classifiers involves augmenting the labelled samples with
synthetic images created by state-of-the-art text-to-image generation models.
Following this trend, we propose Diversified In-domain Synthesis with Efficient
Fine-tuning (DISEF), a novel approach which addresses the generalization
challenge in few-shot learning using synthetic data. DISEF consists of two main
components. First, we propose a novel text-to-image augmentation pipeline that,
by leveraging the real samples and their rich semantics coming from an advanced
captioning model, promotes in-domain sample diversity for better
generalization. Second, we emphasize the importance of effective model
fine-tuning in few-shot recognition, proposing to use Low-Rank Adaptation
(LoRA) for joint adaptation of the text and image encoders in a Vision Language
Model. We validate our method in ten different benchmarks, consistently
outperforming baselines and establishing a new state-of-the-art for few-shot
classification. Code is available at https://github.com/vturrisi/disef.
