Phrase-based Image Captioning
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2085-2094, 2015.
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 linear model to embed an image representation (generated from a previously trained Convolutional Neural Network) into a multimodal space that is common to the images and the phrases that are used to described them. The system is then able to infer phrases from a given image sample. Based on the sentence description 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.