# Segment Using Just One Example

arXiv: https://arxiv.org/abs/2408.07393 · HTML: https://mlsanity.com/paper/2408.07393/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 14 Aug 2024.

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

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data](https://mlsanity.com/paper/1904.00592/) (2019)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)
- [PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering](https://mlsanity.com/paper/2103.17070/) (2021)
- [Foundational Models Defining a New Era in Vision: A Survey and Outlook](https://mlsanity.com/paper/2307.13721/) (2023)
- [Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning](https://mlsanity.com/paper/1703.06452/) (2017)
- [Semantic Segmentation of Remote Sensing Images with Sparse Annotations](https://mlsanity.com/paper/2101.03492/) (2021)
- [Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks](https://mlsanity.com/paper/1609.06846/) (2016)
- [Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks](https://mlsanity.com/paper/1608.00775/) (2016)
- [High-Resolution Semantic Labeling with Convolutional Neural Networks](https://mlsanity.com/paper/1611.01962/) (2016)

## Abstract (arXiv)

Semantic segmentation is an important topic in computer vision with many relevant application in Earth observation. While supervised methods exist, the constraints of limited annotated data has encouraged development of unsupervised approaches. However, existing unsupervised methods resemble clustering and cannot be directly mapped to explicit target classes. In this paper, we deal with single shot semantic segmentation, where one example for the target class is provided, which is used to segment the target class from query/test images. Our approach exploits recently popular Segment Anything (SAM), a promptable foundation model. We specifically design several techniques to automatically generate prompts from the only example/key image in such a way that the segmentation is successfully achieved on a stitch or concatenation of the example/key and query/test images. Proposed technique does not involve any training phase and just requires one example image to grasp the concept. Furthermore, no text-based prompt is required for the proposed method. We evaluated the proposed techniques on building and car classes.