Domain adaptation is especially important for robotics applications, where
target domain training data is usually scarce and annotations are costly to
obtain. We present a method for self-supervised domain adaptation for the
scenario where annotated source domain data (e.g. from synthetic generation) is
available, but the target domain data is completely unannotated. Our method
targets the semantic segmentation task and leverages a segmentation foundation
model (Segment Anything Model) to obtain segment information on unannotated
data. We take inspiration from recent advances in unsupervised local feature
learning and propose an invariance-variance loss structure over the detected
segments for regularizing feature representations in the target domain.
Crucially, this loss structure and network architecture can handle overlapping
segments and oversegmentation as produced by Segment Anything. We demonstrate
the advantage of our method on the challenging YCB-Video and HomebrewedDB
datasets and show that it outperforms prior work and, on YCB-Video, even a
network trained with real annotations.

自主域自适应方法对目标域训练数据稀缺和注释成本高的机器人应用领域尤为重要。我们提出了一种自我监督的域自适应方法，适用于仅有标注源领域数据但目标领域数据完全无标注的情景，并且我们使用了分割基础模型（Segment Anything Model）来获得未标注数据的部分信息，利用无监督局部特征学习的最新进展，提出了一种对目标域中检测到的分割区域进行正则化特征表示的不变性方差损失结构。关键是，这种损失结构和网络架构可以处理由 Segment Anything 生成的重叠分割区域和过分割问题。我们在具有挑战性的 YCB-Video 和 HomebrewedDB 数据集上展示了我们方法的优势，并显示出它优于之前的方法，甚至优于使用真实注释进行训练的网络（在 YCB-Video 上）。