  The main challenge for fine-grained few-shot image classification is to learn
feature representations with higher inter-class and lower intra-class
variations, with a mere few labelled samples. Conventional few-shot learning
methods however cannot be naively adopted for this fine-grained setting -- a
quick pilot study reveals that they in fact push for the opposite (i.e., lower
inter-class variations and higher intra-class variations). To alleviate this
problem, prior works predominately use a support set to reconstruct the query
image and then utilize metric learning to determine its category. Upon careful
inspection, we further reveal that such unidirectional reconstruction methods
only help to increase inter-class variations and are not effective in tackling
intra-class variations. In this paper, we for the first time introduce a
bi-reconstruction mechanism that can simultaneously accommodate for inter-class
and intra-class variations. In addition to using the support set to reconstruct
the query set for increasing inter-class variations, we further use the query
set to reconstruct the support set for reducing intra-class variations. This
design effectively helps the model to explore more subtle and discriminative
features which is key for the fine-grained problem in hand. Furthermore, we
also construct a self-reconstruction module to work alongside the
bi-directional module to make the features even more discriminative.
Experimental results on three widely used fine-grained image classification
datasets consistently show considerable improvements compared with other
methods. Codes are available at: https://github.com/PRIS-CV/Bi-FRN.
