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. |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
|
Attachments
|
|
|
|
|
Notes
|
|
|
|
|
