Cross-Domain Few-Shot Learning has witnessed great stride with the
development of meta-learning. However, most existing methods pay more attention
to learning domain-adaptive inductive bias (meta-knowledge) through
feature-wise manipulation or task diversity improvement while neglecting the
phenomenon that deep networks tend to rely more on high-frequency cues to make
the classification decision, which thus degenerates the robustness of learned
inductive bias since high-frequency information is vulnerable and easy to be
disturbed by noisy information. Hence in this paper, we make one of the first
attempts to propose a Frequency-Aware Prompting method with mutual attention
for Cross-Domain Few-Shot classification, which can let networks simulate the
human visual perception of selecting different frequency cues when facing new
recognition tasks. Specifically, a frequency-aware prompting mechanism is first
proposed, in which high-frequency components of the decomposed source image are
switched either with normal distribution sampling or zeroing to get
frequency-aware augment samples. Then, a mutual attention module is designed to
learn generalizable inductive bias under CD-FSL settings. More importantly, the
proposed method is a plug-and-play module that can be directly applied to most
off-the-shelf CD-FLS methods. Experimental results on CD-FSL benchmarks
demonstrate the effectiveness of our proposed method as well as robustly
improve the performance of existing CD-FLS methods. Resources at
this https URL

通过提出一种基于频率感知提示的方法，该方法使用双向注意力模块来模拟人类视觉感知，在跨领域少样本分类中能够改善现有方法的性能。