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GeoNet is a recently proposed domain adaptation benchmark consisting of threechallenges (i.e., GeoUniDA, GeoImNet, and GeoPlaces). Each challenge containsimages collected from the USA and Asia where there are huge geographical gaps.Our solution adopts a two-stage source-free domain adaptation framework with aSwin Transformer backbone to achieve knowledge transfer from the USA (source)domain to Asia (target) domain. In the first stage, we train a source modelusing labeled source data with a re-sampling strategy and two types ofcross-entropy loss. In the second stage, we generate pseudo labels forunlabeled target data to fine-tune the model. Our method achieves an H-score of74.56% and ultimately ranks 1st in the GeoUniDA challenge. In GeoImNet andGeoPlaces challenges, our solution also reaches a top-3 accuracy of 64.46% and51.23%, respectively.

Self-training solutions for the ICCV 2023 GeoNet Challenge