Knowledge-based Fine-Grained Classification for Few-Shot Learning

The small inter-class variance and the large intra-class variance make the few-shot and fine-grained image classification more difficult because the machine cannot obtain enough information from only a few images. The external knowledge contains more semantics and can support the model to extract important features, while most of existing few-shot learning algorithms only focus on leveraging the visual features from images, little attention has been paid to the cross-modal external knowledge. In this paper, we propose a knowledge-based fine-grained classification mechanism for few-shot learning, which can overcome the difficulty of only obtaining limited and discriminative features from unimodal samples. We extract the visual features and the knowledge features from textual descriptions and a domain-specific knowledge graph at global and local levels to build the semantic space. To tackle the gap between multimodal features, we propose a mirror framework, named Mirror Mapping Network (MMN), to map the multimodal features into the same semantic space with two directions. Extensive experimental results show that our method outperforms the state-of-the-art.