BAP: Bimodal Attribute Prediction for Zero-Shot Image Categorization

Recent advances in attribute-based methods provide the zero-shot learning problem with practical solutions. In attribute-based methods, visual attributes are introduced to fill the gap between low-level image features and high-level semantic information. This paper proposes a novel bimodal attribute prediction model called BAP, which can better predict visual attributes in images. BAP fuses advantages of the conventional direct attribute prediction (DAP) and indirect attribute prediction (IAP) on the level of attribute prediction. It contains an attribute-classifier pooling process that generates a large amount of base classifiers and a combination strategy that integrates these classifiers. We explore and propose four BAP models with different combination strategies in this paper, and experimentally show that our BAP outperforms the conventional models both in offline and online zero-shot image categorization.