Class Discriminative Feature Embedding For Meta Learning based Few Shot Classification
Although deep learning based approaches have been very effective in solving problems with plenty of labeled data, they suffer in tackling problems for which labeled data are scarce. In few shot classification, the objective is to train a classifier from only a handful of labeled examples in a support set. In this paper, we propose a few shot learning framework based on structured margin loss which takes into account the global structure of the support set in order to generate a highly discriminative feature space where the features from distinct classes are well separated in clusters. Moreover, in our meta learning based framework, we propose a context aware query embedding encoder for incorporating support set context into query embedding and generating more discriminative and task dependent query embeddings. The task dependent features help the metalearner to learn a distribution over tasks more effectively. Extensive experiments based on few shot, zero shot and semi supervised learning on three benchmarks show the advantages of the proposed model compared to state of the art.
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)