Domain Adaptation via Bidirectional Cross-Attention Transformer

Understand

Domain Adaptation (DA) aims to leverage the knowledge learned from a source domain with ample labeled data to a target domain with unlabeled data only.

• Most existing studies on DA contribute to learning domain-invariant feature representations for both domains by minimizing the domain gap based on convolution-based neural networks.
• Recently, vision transformers significantly improved performance in multiple vision tasks.
• Built on vision transformers, in this paper we propose a Bidirectional Cross-Attention Transformer (BCAT) for DA with the aim to improve the performance.