  Few-shot learning problem focuses on recognizing unseen classes given a few
labeled images. In recent effort, more attention is paid to fine-grained
feature embedding, ignoring the relationship among different distance metrics.
In this paper, for the first time, we investigate the contributions of
different distance metrics, and propose an adaptive fusion scheme, bringing
significant improvements in few-shot classification. We start from a naive
baseline of confidence summation and demonstrate the necessity of exploiting
the complementary property of different distance metrics. By finding the
competition problem among them, built upon the baseline, we propose an Adaptive
Metrics Module (AMM) to decouple metrics fusion into metric-prediction fusion
and metric-losses fusion. The former encourages mutual complementary, while the
latter alleviates metric competition via multi-task collaborative learning.
Based on AMM, we design a few-shot classification framework AMTNet, including
the AMM and the Global Adaptive Loss (GAL), to jointly optimize the few-shot
task and auxiliary self-supervised task, making the embedding features more
robust. In the experiment, the proposed AMM achieves 2% higher performance than
the naive metrics fusion module, and our AMTNet outperforms the
state-of-the-arts on multiple benchmark datasets.
