Generating a novel textual description of an image is an interesting problem
that connects computer vision and natural language processing. In this paper,
we present a simple model that is able to generate descriptive sentences given
a sample image. This model has a strong focus on the syntax of the
descriptions. We train a purely bilinear model that learns a metric between an
image representation (generated from a previously trained Convolutional Neural
Network) and phrases that are used to described them. The system is then able
to infer phrases from a given image sample. Based on caption syntax statistics,
we propose a simple language model that can produce relevant descriptions for a
given test image using the phrases inferred. Our approach, which is
considerably simpler than state-of-the-art models, achieves comparable results
in two popular datasets for the task: Flickr30k and the recently proposed
Microsoft COCO