# Meta-DRN: Meta-Learning for 1-Shot Image Segmentation

arXiv: https://arxiv.org/abs/2008.00247 · HTML: https://mlsanity.com/paper/2008.00247/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples](https://mlsanity.com/paper/2112.02763/) (2021)
- [MetaDance: Few-shot Dancing Video Retargeting via Temporal-aware Meta-learning](https://mlsanity.com/paper/2201.04851/) (2022)

## Abstract (arXiv)

Modern deep learning models have revolutionized the field of computer vision. But, a significant drawback of most of these models is that they require a large number of labelled examples to generalize properly. Recent developments in few-shot learning aim to alleviate this requirement. In this paper, we propose a novel lightweight CNN architecture for 1-shot image segmentation. The proposed model is created by taking inspiration from well-performing architectures for semantic segmentation and adapting it to the 1-shot domain. We train our model using 4 meta-learning algorithms that have worked well for image classification and compare the results. For the chosen dataset, our proposed model has a 70% lower parameter count than the benchmark, while having better or comparable mean IoU scores using all 4 of the meta-learning algorithms.