We propose a method that learns a discriminative yet semantic space for object categorization, where we also embed auxiliary semantic entities such as supercategories and attributes. Contrary to prior work which only utilized them as side information, we explicitly embed the semantic entities into the same space where we embed categories, which enables us to represent a category as their linear combination. By exploiting such a unified model for semantics, we enforce each category to be represented by a supercategory + sparse combination of attributes, with an additional exclusive regularization to learn discriminative composition.
Furthermore, REF proposed a discriminative learning framework that can give category embeddings by approximating each category embeddings as a sum of its direct supercategory plus a sparse combination of attributes.
A Unified Semantic Embedding: Relating Taxonomies and Attributes
ArXiv
ArXiv
Computer Science