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 [BibTeX] [Marc21]
Phrase-based Image Captioning
Type of publication: Conference paper
Citation: Lebret_ICML_2015
Publication status: Published
Booktitle: International Conference on Machine Learning (ICML)
Volume: 37
Year: 2015
Pages: 2085–2094
Publisher: JMLR
Location: Lille, France
Crossref: Lebret_Idiap-RR-08-2015:
URL: http://jmlr.org/proceedings/pa...
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, Rémi
Pinheiro, Pedro H. O.
Collobert, Ronan
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Total mark: 0
  • Lebret_ICML_2015.pdf