  Few-shot learning aims to recognize novel queries with limited support
samples by learning from base knowledge. Recent progress in this setting
assumes that the base knowledge and novel query samples are distributed in the
same domains, which are usually infeasible for realistic applications. Toward
this issue, we propose to address the cross-domain few-shot learning problem
where only extremely few samples are available in target domains. Under this
realistic setting, we focus on the fast adaptation capability of meta-learners
by proposing an effective dual adaptive representation alignment approach. In
our approach, a prototypical feature alignment is first proposed to recalibrate
support instances as prototypes and reproject these prototypes with a
differentiable closed-form solution. Therefore feature spaces of learned
knowledge can be adaptively transformed to query spaces by the cross-instance
and cross-prototype relations. Besides the feature alignment, we further
present a normalized distribution alignment module, which exploits prior
statistics of query samples for solving the covariant shifts among the support
and query samples. With these two modules, a progressive meta-learning
framework is constructed to perform the fast adaptation with extremely few-shot
samples while maintaining its generalization capabilities. Experimental
evidence demonstrates our approach achieves new state-of-the-art results on 4
CDFSL benchmarks and 4 fine-grained cross-domain benchmarks.
