Few-shot classification is a challenging problem as only very few training
examples are given for each new task. One of the effective research lines to
address this challenge focuses on learning deep representations driven by a
similarity measure between a query image and few support images of some class.
Statistically, this amounts to measure the dependency of image features, viewed
as random vectors in a high-dimensional embedding space. Previous methods
either only use marginal distributions without considering joint distributions,
suffering from limited representation capability, or are computationally
expensive though harnessing joint distributions. In this paper, we propose a
deep Brownian Distance Covariance (DeepBDC) method for few-shot classification.
The central idea of DeepBDC is to learn image representations by measuring the
discrepancy between joint characteristic functions of embedded features and
product of the marginals. As the BDC metric is decoupled, we formulate it as a
highly modular and efficient layer. Furthermore, we instantiate DeepBDC in two
different few-shot classification frameworks. We make experiments on six
standard few-shot image benchmarks, covering general object recognition,
fine-grained categorization and cross-domain classification. Extensive
evaluations show our DeepBDC significantly outperforms the counterparts, while
establishing new state-of-the-art results. The source code is available at
this http URL

该研究提出了一种深层布朗运动距离协方差（DeepBDC）方法来解决少样本分类问题，其关键思想是通过测量嵌入特征的联合特征函数与边缘特征的乘积之间的差异来学习图像表示，实验表明 DeepBDC 显着优于同类方法，创造了新的最先进结果。