Multi-label Few-shot Learning with Semantic Inference (Student Abstract)

Abstract

Few-shot learning can adapt the classification model to new labels with only a few labeled examples. Previous studies mainly focused on the scenario of a single category label per example but have not effectively solved the more challenging multi-label scenario, which has exponential-sized output space and low-data. In this paper, we propose a semantic-aware meta-learning model for multi-label few-shot learning. Our approach can learn and infer the semantic correlation between unseen labels and historical labels to quickly adapt multi-label tasks based on only a few examples. Specifically, features can be mapped into the semantic space via label embeddings to exploit the label correlation, thus structuring the overwhelming output space. We design a novel semantic inference mechanism for leveraging prior knowledge learned from historical labels, which will produce good generalization performance on new labels to alleviate the overfitting caused by low-data. Finally, empirical results show that the proposed method significantly outperforms the existing state-of-the-art methods on the multi-label few-shot learning tasks.