Cooperative Bi-path Metric for Few-shot Learning
Zeyuan Wang, Yifan Zhao, Jia Li*,
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University
School of Electronics Engineering and Computer Science, Peking University
Given base classes with sufficient labeled samples, the target of
few-shot classification is to recognize unlabeled samples of novel
classes with only a few labeled samples. Novel classes and scarcity
of labeled samples make few-shot classification much challenging.
Most of existing methods only pay attention to the relationship
between labeled and unlabeled samples of novel classes, which
don’t make full use of information within base classes. In this paper,
we make two contributions to investigate few-shot classification
problem. First, we report a simple and effective baseline trained on
base classes in the way of traditional supervised learning, which
can achieve comparable results to the state of the art. Second, based
on the baseline, we propose a coorperative bi-path metric for classification, which leverages the correlations between base classes
and novel classes to further improve the accuracy. Experiments on
two widely used benchmarks show that our method is a simple and
effective framework, and a new state of the art is established in the
few-shot classification field.
Illustration of the pipeline of Cooperative Bi-path Metric. The top half part upon the red dotted line represents our proposed transductive similarity, and the bottom half
part is the classic inductive similarity. ρsupport refers to the similarity distributions of support set on base classes, and ρquery refers to those of query set. The final classification score ψ is the weighted sum of ϕ and φ, where ϕ is the usual
cosine similarity between support set and query set, and φ is the similarity between ρsupport and ρquery.
Comparison to prior works on 5-way classification on miniImageNet benchmark. ConvNet is a 4-layer
convolutional network, WideResNet is a wider version of
ResNet, baseline++ denotes our baseline with tricks and
CBM (CBM+LLE) indicates Coorperative Bi-path Metric
(with LLE). All the numbers of prior works are imported
from corresponding original papers. Best results are bolded
2020/08: We have updated the webpage.
Zeyuan Wang, Yifan Zhao, Jia Li*, and Yonghong Tian. Cooperative Bi-path Metric for Few-shot Learning.