  Deep neural networks have demonstrated advanced abilities on various visual
classification tasks, which heavily rely on the large-scale training samples
with annotated ground-truth. However, it is unrealistic always to require such
annotation in real-world applications. Recently, Few-Shot learning (FS), as an
attempt to address the shortage of training samples, has made significant
progress in generic classification tasks. Nonetheless, it is still challenging
for current FS models to distinguish the subtle differences between
fine-grained categories given limited training data. To filling the
classification gap, in this paper, we address the Few-Shot Fine-Grained (FSFG)
classification problem, which focuses on tackling the fine-grained
classification under the challenging few-shot learning setting. A novel
low-rank pairwise bilinear pooling operation is proposed to capture the nuanced
differences between the support and query images for learning an effective
distance metric. Moreover, a feature alignment layer is designed to match the
support image features with query ones before the comparison. We name the
proposed model Low-Rank Pairwise Alignment Bilinear Network (LRPABN), which is
trained in an end-to-end fashion. Comprehensive experimental results on four
widely used fine-grained classification datasets demonstrate that our LRPABN
model achieves the superior performances compared to state-of-the-art methods.
