# Relational Generalized Few-Shot Learning

arXiv: https://arxiv.org/abs/1907.09557 · HTML: https://mlsanity.com/paper/1907.09557/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting](https://mlsanity.com/paper/2108.08165/) (2021)
- [Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning](https://mlsanity.com/paper/2202.09059/) (2022)
- [End-to-end One-shot Human Parsing](https://mlsanity.com/paper/2105.01241/) (2021)
- [Better Generalized Few-Shot Learning Even Without Base Data](https://mlsanity.com/paper/2211.16095/) (2022)
- [A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning](https://mlsanity.com/paper/2110.11128/) (2021)
- [BaseTransformers: Attention over base data-points for One Shot Learning](https://mlsanity.com/paper/2210.02476/) (2022)

## Abstract (arXiv)

Transferring learned models to novel tasks is a challenging problem, particularly if only very few labeled examples are available. Although this few-shot learning setup has received a lot of attention recently, most proposed methods focus on discriminating novel classes only. Instead, we consider the extended setup of generalized few-shot learning (GFSL), where the model is required to perform classification on the joint label space consisting of both previously seen and novel classes. We propose a graph-based framework that explicitly models relationships between all seen and novel classes in the joint label space. Our model Graph-convolutional Global Prototypical Networks (GcGPN) incorporates these inter-class relations using graph-convolution in order to embed novel class representations into the existing space of previously seen classes in a globally consistent manner. Our approach ensures both fast adaptation and global discrimination, which is the major challenge in GFSL. We demonstrate the benefits of our model on two challenging benchmark datasets.