A Novel Method of Data and Feature Enhancement for Few-Shot Image Classification

摘要

Deep learning has shown remarkable performance in quantity of vision tasks. However, its large network generally requires quantity of samples to support sufficient parameters learning during training process. Such high request greatly reduces efficiency when applying on a small dataset with few samples. To alleviate this problem, we propose a novel data enhancement method for few-shot learning via a cutout approach and feature enhancement. After enhancement, the generated network not only produces distinguish feature map without collecting more samples, but also achieves advantage of feature representation with high efficiency for computing. Specifically, cutout approach is simple yet highly effective for image regulation, which enhances input image matrix by adding a fixed mask to improve robustness and overall performance of network. Afterward, we perform feature enhancement by proposing a feature promotion module, which uses characteristics of dilated convolution and sequential processing to improve feature representation ability, thus improving efficiency of the whole network. We conduct comparative experiments on both miniImageNet and CUB datasets, where the proposed method is superior to comparative methods in both 1-shot and 5-shot cases.

巫义锐

青年教授, CCF 高级会员

My research interests include Computer Vision, Artifical Intelligence, Multimedia Computing and Intelligent Water Conservancy.