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 [BibTeX] [Marc21]
Phrase-based Image Captioning
Type of publication: Idiap-RR
Citation: Lebret_Idiap-RR-08-2015
Number: Idiap-RR-08-2015
Year: 2015
Month: 5
Institution: Idiap
Note: Under review by the International Conference on Machine Learning (ICML).
Abstract: 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.
Projects Idiap
Authors Lebret, RĂ©mi
Pinheiro, Pedro H. O.
Collobert, Ronan
Crossref by Lebret_ICML_2015
Added by: [ADM]
Total mark: 0
  • Lebret_Idiap-RR-08-2015.pdf (MD5: 9a063c3633258091fb19312ac94cca78)