  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. Most existing methods only pay attention to the
relationship between labeled and unlabeled samples of novel classes, which do
not make full use of information within base classes. In this paper, we make
two contributions to investigate the 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 cooperative
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.
