# RAFIC: Retrieval-Augmented Few-shot Image Classification

arXiv: https://arxiv.org/abs/2312.06868 · HTML: https://mlsanity.com/paper/2312.06868/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification](https://mlsanity.com/paper/2207.09519/) (2022)
- [A Baseline for Few-Shot Image Classification](https://mlsanity.com/paper/1909.02729/) (2019)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks](https://mlsanity.com/paper/1810.10863/) (2018)
- [Retrieval Augmented Classification for Long-Tail Visual Recognition](https://mlsanity.com/paper/2202.11233/) (2022)
- [Few-Shot Learning via Saliency-guided Hallucination of Samples](https://mlsanity.com/paper/1904.03472/) (2019)
- [Data Augmentation for Meta-Learning](https://mlsanity.com/paper/2010.07092/) (2020)
- [Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models](https://mlsanity.com/paper/2006.10236/) (2020)
- [One-Shot Recognition of Manufacturing Defects in Steel Surfaces](https://mlsanity.com/paper/2005.05815/) (2020)
- [Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images](https://mlsanity.com/paper/2105.11874/) (2021)

## Built on by (most cited first, 1 of 1)

- [GarmentAligner: Text-to-Garment Generation via Retrieval-augmented Multi-level Corrections](https://mlsanity.com/paper/2408.12352/) (2024)

## Abstract (arXiv)

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.