Simple Image Description Generator via a Linear Phrase-based Model
| Type of publication: | Idiap-RR |
| Citation: | Lebret_Idiap-RR-22-2015 |
| Number: | Idiap-RR-22-2015 |
| Year: | 2015 |
| Month: | 6 |
| Institution: | Idiap |
| Note: | In the workshop session of the International Conference on Learning Representations |
| 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 on the recently release Microsoft COCO dataset. |
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| Projects |
Idiap |
| Authors | |
| Added by: | [ADM] |
| Total mark: | 0 |
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