REPORT Lebret_Idiap-RR-22-2015/IDIAP Simple Image Description Generator via a Linear Phrase-based Model Lebret, Rémi Pinheiro, Pedro H. O. Collobert, Ronan EXTERNAL http://publications.idiap.ch/attachments/reports/2015/Lebret_Idiap-RR-22-2015.pdf PUBLIC Idiap-RR-22-2015 2015 Idiap June 2015 In the workshop session of the International Conference on Learning Representations 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 on the recently release Microsoft COCO dataset.