Enhancing Semi-Supervised Learning with Cross-Modal Knowledge

Abstract

Semi-supervised learning (SSL), which leverages a small number of labeled data that rely on expert knowledge and a large number of easily accessible unlabeled data, has made rapid progress recently. However, the information comes from a single modality and the corresponding labels are in form of one-hot in pre-existing SSL approaches, which can easily lead to deficiency supervision, omission of information and unsatisfactory results, especially when more categories and less labeled samples are covered. In this paper, we propose a novel method to further enhance SSL by introducing semantic modal knowledge, which contains the word embeddings of class labels and the semantic hierarchy structure among classes. The former helps retain more potential information and almost quantitatively reflects the similarities and differences between categories. The later encourages the model to construct the classification edge from simple to complex, and thus improves the generalization ability of the model. Comprehensive experiments and ablation studies are conducted on commonly-used datasets to demonstrate the effectiveness of our method.