Towards Fine-Grained Open Zero-Shot Learning: Inferring Unseen Visual Features from Attributes

Abstract

Zero-shot Learning (ZSL) can leverage attributes to recognise unseen instances. However, the training data is limited and cannot adequately discriminate fine-grained classes with similar attributes. In this paper, we propose a complementary procedure that inversely makes use of attributes to infer discriminative visual features for unseen classes. In this way, ZSL is fully converted into conventional supervised classification, where robust classifiers can be employed to address the fine-grained problem. To infer high-quality unseen data, we propose a novel algorithm named Orthogonal Semantic-Visual Embedding (OSVE) that can discover the tiny visual differences between different instances under the same attribute by an orthogonal embedding space. On two fine-grained benchmarks, CUB and SUN, our method remarkably improves the state-of-the-art results under standard ZSL settings. We further challenge the Open ZSL problem where the number of seen classes is significantly smaller than that of unseen classes. Substantial experiments manifest that the inferred visual features can be successfully fed to SVM which can effectively discriminate unseen classes from fine-grained open candidates.