Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective

Jinxiang Lai, Siqian Yang, GUANNAN JIANG, Xi Wang, Yuxi Li, Xiaochen Chen, Jun Liu

Abstract:

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.