  We propose a novel approach to improve a visual-semantic embedding model by
incorporating concept representations captured from an external structured
knowledge base. We investigate its performance on image classification under
both standard and zero-shot settings. We propose two novel evaluation
frameworks to analyse classification errors with respect to the class hierarchy
indicated by the knowledge base. The approach is tested using the ILSVRC 2012
image dataset and a WordNet knowledge base. With respect to both standard and
zero-shot image classification, our approach shows superior performance
compared with the original approach, which uses word embeddings.
