  GeoNet is a recently proposed domain adaptation benchmark consisting of three
challenges (i.e., GeoUniDA, GeoImNet, and GeoPlaces). Each challenge contains
images collected from the USA and Asia where there are huge geographical gaps.
Our solution adopts a two-stage source-free domain adaptation framework with a
Swin Transformer backbone to achieve knowledge transfer from the USA (source)
domain to Asia (target) domain. In the first stage, we train a source model
using labeled source data with a re-sampling strategy and two types of
cross-entropy loss. In the second stage, we generate pseudo labels for
unlabeled target data to fine-tune the model. Our method achieves an H-score of
74.56% and ultimately ranks 1st in the GeoUniDA challenge. In GeoImNet and
GeoPlaces challenges, our solution also reaches a top-3 accuracy of 64.46% and
51.23%, respectively.
