We study source-free unsupervised domain adaptation (SFUDA) for semantic
segmentation, which aims to adapt a source-trained model to the target domain
without accessing the source data. Many works have been proposed to address
this challenging problem, among which uncertainty-based self-training is a
predominant approach. However, without comprehensive denoising mechanisms, they
still largely fall into biased estimates when dealing with different domains
and confirmation bias. In this paper, we observe that pseudo-label noise is
mainly contained in unstable samples in which the predictions of most pixels
undergo significant variations during self-training. Inspired by this, we
propose a novel mechanism to denoise unstable samples with stable ones.
Specifically, we introduce the Stable Neighbor Denoising (SND) approach, which
effectively discovers highly correlated stable and unstable samples by nearest
neighbor retrieval and guides the reliable optimization of unstable samples by
bi-level learning. Moreover, we compensate for the stable set by object-level
object paste, which can further eliminate the bias caused by less learned
classes. Our SND enjoys two advantages. First, SND does not require a specific
segmentor structure, endowing its universality. Second, SND simultaneously
addresses the issues of class, domain, and confirmation biases during
adaptation, ensuring its effectiveness. Extensive experiments show that SND
consistently outperforms state-of-the-art methods in various SFUDA semantic
segmentation settings. In addition, SND can be easily integrated with other
approaches, obtaining further improvements.

我们研究了无源无监督领域自适应（SFUDA）用于语义分割的问题，提出了一种基于稳定邻居去噪的方法，并结合对象级对象粘贴来解决类别、域和确认偏差问题。通过广泛的实验证明，该方法在各种 SFUDA 语义分割设置中始终优于最先进的方法。