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.

这篇论文主要讨论了利用少量样本进行图像变化检测的问题，并通过使用简单的图像处理方法生成合成但具有信息量的数据集并设计了基于目标检测的早期融合网络来解决数据集不足导致的泛化能力差的问题。研究结果表明，使用合成数据训练的模型具有更好的泛化能力，且在此基础上利用少量样本进行微调能够获得优秀的结果。