# Image change detection with only a few samples

arXiv: https://arxiv.org/abs/2311.03762 · HTML: https://mlsanity.com/paper/2311.03762/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection](https://mlsanity.com/paper/1708.01642/) (2017)
- [Playing for Benchmarks](https://mlsanity.com/paper/1709.07322/) (2017)
- [Learning to Compare Image Patches via Convolutional Neural Networks](https://mlsanity.com/paper/1504.03641/) (2015)
- [Effective Use of Synthetic Data for Urban Scene Semantic Segmentation](https://mlsanity.com/paper/1807.06132/) (2018)
- [Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications](https://mlsanity.com/paper/1708.05869/) (2017)
- [On Rendering Synthetic Images for Training an Object Detector](https://mlsanity.com/paper/1411.7911/) (2014)
- [Change Detection between Multimodal Remote Sensing Data Using Siamese CNN](https://mlsanity.com/paper/1807.09562/) (2018)
- [Spot the Difference by Object Detection](https://mlsanity.com/paper/1801.01051/) (2018)

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

- [Revisiting Change Detection Methods for their Application to Serac Fall Time-Lapse Monitoring](https://mlsanity.com/paper/2605.28100/) (2026)

## Abstract (arXiv)

This paper considers image change detection with only a small number of samples, which is a significant problem in terms of a few annotations available. A major impediment of image change detection task is the lack of large annotated datasets covering a wide variety of scenes. Change detection models trained on insufficient datasets have shown poor generalization capability. To address the poor generalization issue, we propose using simple image processing methods for generating synthetic but informative datasets, and design an early fusion network based on object detection which could outperform the siamese neural network. Our key insight is that the synthetic data enables the trained model to have good generalization ability for various scenarios. We compare the model trained on the synthetic data with that on the real-world data captured from a challenging dataset, CDNet, using six different test sets. The results demonstrate that the synthetic data is informative enough to achieve higher generalization ability than the insufficient real-world data. Besides, the experiment shows that utilizing a few (often tens of) samples to fine-tune the model trained on the synthetic data will achieve excellent results.