Universal domain adaptation (UniDA) aims to transfer knowledge from the
source domain to the target domain without any prior knowledge about the label
set.
The challenge lies in how to determine whether the target samples belong to
common categories.
The mainstream methods make judgments based on the sample features, which
overemphasizes global information while ignoring the most crucial local objects
in the image, resulting in limited accuracy.
To address this issue,
we propose a Universal Attention Matching (UniAM) framework by exploiting the
self-attention mechanism in vision transformer to capture the crucial object
information.
The proposed framework introduces a novel Compressive Attention Matching
(CAM) approach to explore the core information by compressively representing
attentions.
Furthermore, CAM incorporates a residual-based measurement to determine the
sample commonness. By utilizing the measurement, UniAM achieves domain-wise and
category-wise Common Feature Alignment (CFA) and Target Class Separation (TCS).
Notably, UniAM is the first method utilizing the attention in vision
transformer directly to perform classification tasks.
Extensive experiments show that UniAM outperforms the current
state-of-the-art methods on various benchmark datasets.

本文提出了一种基于自注意力机制、综合注意力匹配和压缩表示的普适域自适应框架，可用于分类任务，并在多个基准数据集上取得了领先于现有方法的性能。