  Few-shot learning (FSL), which aims to recognise new classes by adapting the
learned knowledge with extremely limited few-shot (support) examples, remains
an important open problem in computer vision. Most of the existing methods for
feature alignment in few-shot learning only consider image-level or
spatial-level alignment while omitting the channel disparity. Our insight is
that these methods would lead to poor adaptation with redundant matching, and
leveraging channel-wise adjustment is the key to well adapting the learned
knowledge to new classes. Therefore, in this paper, we propose to learn a
dynamic alignment, which can effectively highlight both query regions and
channels according to different local support information. Specifically, this
is achieved by first dynamically sampling the neighbourhood of the feature
position conditioned on the input few shot, based on which we further predict a
both position-dependent and channel-dependent Dynamic Meta-filter. The filter
is used to align the query feature with position-specific and channel-specific
knowledge. Moreover, we adopt Neural Ordinary Differential Equation (ODE) to
enable a more accurate control of the alignment. In such a sense our model is
able to better capture fine-grained semantic context of the few-shot example
and thus facilitates dynamical knowledge adaptation for few-shot learning. The
resulting framework establishes the new state-of-the-arts on major few-shot
visual recognition benchmarks, including miniImageNet and tieredImageNet.
