  Nowadays, transformer-based models gradually become the default choice for
artificial intelligence pioneers. The models also show superiority even in the
few-shot scenarios. In this paper, we revisit the classical methods and propose
a new few-shot alternative. Specifically, we investigate the few-shot one-class
problem, which actually takes a known sample as a reference to detect whether
an unknown instance belongs to the same class. This problem can be studied from
the perspective of sequence match. It is shown that with meta-learning, the
classical sequence match method, i.e. Compare-Aggregate, significantly
outperforms transformer ones. The classical approach requires much less
training cost. Furthermore, we perform an empirical comparison between two
kinds of sequence match approaches under simple fine-tuning and meta-learning.
Meta-learning causes the transformer models' features to have high-correlation
dimensions. The reason is closely related to the number of layers and heads of
transformer models. Experimental codes and data are available at
https://github.com/hmt2014/FewOne
