  Source-free Unsupervised Domain Adaptation (SFDA) aims to classify target
samples by only accessing a pre-trained source model and unlabelled target
samples. Since no source data is available, transferring the knowledge from the
source domain to the target domain is challenging. Existing methods normally
exploit the pair-wise relation among target samples and attempt to discover
their correlations by clustering these samples based on semantic features. The
drawback of these methods includes: 1) the pair-wise relation is limited to
exposing the underlying correlations of two more samples, hindering the
exploration of the structural information embedded in the target domain; 2) the
clustering process only relies on the semantic feature, while overlooking the
critical effect of domain shift, i.e., the distribution differences between the
source and target domains. To address these issues, we propose a new SFDA
method that exploits the high-order neighborhood relation and explicitly takes
the domain shift effect into account. Specifically, we formulate the SFDA as a
Hypergraph learning problem and construct hyperedges to explore the local group
and context information among multiple samples. Moreover, we integrate a
self-loop strategy into the constructed hypergraph to elegantly introduce the
domain uncertainty of each sample. By clustering these samples based on
hyperedges, both the semantic feature and domain shift effects are considered.
We then describe an adaptive relation-based objective to tune the model with
soft attention levels for all samples. Extensive experiments are conducted on
Office-31, Office-Home, VisDA, and PointDA-10 datasets. The results demonstrate
the superiority of our method over state-of-the-art counterparts.
