Contextual similarity-based multi-level second-order attention network for semi-supervised few-shot learning

Abstract

In this paper, we tackle the few-shot learning problem in a semi-supervised setting where a limited number of labeled data-points and a number of low-cost unlabeled samples are assumed to be available. In particular, some of the unlabeled samples share the same label space with the support set, referring to as known samples, while some of them are from distracter classes, referring to as unknown ones. The keys are how to learn a powerful representation and how to pick and label unlabeled known instances to construct discriminative classifiers. We address both issues by learning multi-level second-order attention representation followed by a contextual similarity. We first develop a novel trainable multi-level second-order attention network(MSAN) to adaptively learn more powerful feature representation by using second-order feature statistics. Our proposed MSAN is able to better represent the samples while the parameter is not increased. Then we introduce a contextual measure that considers not only the pair-wise relationship but also the task-specific condition, to calculate the similarity between unlabeled samples and each support class, thus to label and pick the quasi-known samples. The hypothesis is that the known unlabeled sample should not only be strongly similar to one particular class, but also be significantly dissimilar to other classes. With the combination of support set and pseudo-labeled set, we train an episodic linear classifier for each episode and the parameters of multi-level second-order attention network are updated by minimizing the loss of the query set. Extensive experiments on four popular benchmarks (Omniglot, miniImageNet, tieredImageNet, and CUB-200-2011) demonstrate that our simple yet effective approach can achieve competitive accuracy compared to the state-of-the-art methods.