When Daformer Meets Multi-Modality Datasets

We introduce innovative unsupervised domain adaptation (UDA) techniques that leverage the integration of DAFomer and cross-attention mechanisms, tailored to effectively handle multi-modal datasets. We investigate the methods on the FLAIR-1 dataset with different domains, including RGB, NIR bands, and height information. Experimental findings strongly support the idea that integrating inter-modal information significantly enhances segmentation accuracy, making the model more versatile and effective in handling multi-modal datasets across diverse conditions and domains.