ICCV Challenge on Geographical Domain Adaptation [R]

As part of ICCV 2023 in Paris, this year we are organizing a challenge on solving domain gaps that occur when computer vision models are transferred across geographical locations. The challenge covers three tracks in unsupervised scene adaptation, image adaptation and universal adaptation. The challenge is open to everyone, with attractive prizes for the winners. Check it out at the following links!

Challenge Rules and Guidelines: [https://geonet-challenge.github.io/ICCV2023/challenge.html](https://geonet-challenge.github.io/ICCV2023/challenge.html)
Challenge Registration: [https://forms.gle/zSZA1iaPD3mZxjyn7](https://forms.gle/zSZA1iaPD3mZxjyn7)
Code and baselines: [https://github.com/ViLab-UCSD/GeoNet](https://github.com/ViLab-UCSD/GeoNet)

The training data for the challenge …

challenge computer computer vision domain adaptation iccv image locations machinelearning paris part unsupervised vision vision models