Abstract
Annotating instance masks is time-consuming and labor-intensive. A promising solution is to predict contours using a deep learning model and then allow users to refine them. However, most existing methods focus on in-domain scenarios, limiting their effectiveness for cross-domain annotation tasks. In this paper, we propose SiamAnno, a framework inspired by the use of Siamese networks in object tracking. SiamAnno leverages one-shot learning to annotate previously unseen objects by taking a bounding box as input and predicting object boundaries, which can then be adjusted by annotators. Trained on one dataset and tested on another without fine-tuning, SiamAnno achieves state-of-the-art (SOTA) performance across multiple datasets, demonstrating its ability to handle domain and environment shifts in cross-domain tasks. We also provide more comprehensive results compared to previous work, establishing a strong baseline for future research. To our knowledge, SiamAnno is the first model to explore Siamese architecture for instance annotation.
Abstract (translated)
标注实例掩模是一项耗时且费力的工作。一个有前景的解决方案是使用深度学习模型预测轮廓，然后允许用户进行调整。然而，大多数现有的方法主要关注于领域内的场景，这限制了它们在跨域标注任务中的有效性。在这篇论文中，我们提出了SiamAnno框架，该框架借鉴了Siamese网络在物体跟踪中的应用。SiamAnno利用一次性学习的方法，通过输入一个边界框来预测未见过的对象边界，并允许注释者进行调整。经过一个数据集的训练并在另一个未经微调的数据集中测试后，SiamAnno在多个数据集中取得了最先进的（SOTA）性能，展示了其处理跨域任务中领域和环境变化的能力。我们还提供了比先前工作更全面的结果，为未来的研究奠定了坚实的基础。据我们所知，SiamAnno是第一个探索Siamese架构用于实例标注的模型。
URL
https://arxiv.org/abs/2505.03184
