  In this paper, we mainly focus on the problem of how to learn additional
feature representations for few-shot image classification through pretext tasks
(e.g., rotation or color permutation and so on). This additional knowledge
generated by pretext tasks can further improve the performance of few-shot
learning (FSL) as it differs from human-annotated supervision (i.e., class
labels of FSL tasks). To solve this problem, we present a plug-in Hierarchical
Tree Structure-aware (HTS) method, which not only learns the relationship of
FSL and pretext tasks, but more importantly, can adaptively select and
aggregate feature representations generated by pretext tasks to maximize the
performance of FSL tasks. A hierarchical tree constructing component and a
gated selection aggregating component is introduced to construct the tree
structure and find richer transferable knowledge that can rapidly adapt to
novel classes with a few labeled images. Extensive experiments show that our
HTS can significantly enhance multiple few-shot methods to achieve new
state-of-the-art performance on four benchmark datasets. The code is available
at: https://github.com/remiMZ/HTS-ECCV22.
