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.

翻译：本文研究了在仅有少量样本情况下的图像变化检测问题，这在小标注数据场景中具有重要意义。图像变化检测任务的主要障碍在于缺乏覆盖广泛场景的大规模标注数据集。在不足数据集上训练的变化检测模型泛化能力较差。为解决泛化能力不足的问题，我们提出使用简单的图像处理方法生成合成但信息丰富的数据集，并设计了一种基于目标检测的早期融合网络，该网络能够超越孪生神经网络的表现。我们的核心见解在于：合成数据使得训练模型能够在各种场景下具备良好的泛化能力。我们将基于合成数据训练的模型与基于CDNet这一具有挑战性的真实数据集训练的模型在六个不同测试集上进行了对比。结果表明，合成数据的信息量足以比不足的真实数据实现更高的泛化能力。此外，实验证明，利用少量（通常为数十个）样本对基于合成数据训练的模型进行微调，即可获得优异的结果。