Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain Adaptation via Bidirectional Cross-Attention Transformer   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. In the proposed BCAT, the attention
mechanism can extract implicit source and target mix-up feature representations
to narrow the domain discrepancy. Specifically, in BCAT, we design a
weight-sharing quadruple-branch transformer with a bidirectional
cross-attention mechanism to learn domain-invariant feature representations.
Extensive experiments demonstrate that the proposed BCAT model achieves
superior performance on four benchmark datasets over existing state-of-the-art
DA methods that are based on convolutions or transformers.

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The categories of this paper are: supr-con
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
3
The categories of this paper are: physics
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin