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arXiv · 1804.07889

Entity-aware Image Caption Generation

Di Lu·Spencer Whitehead·Lifu Huang·Heng Ji·Shih-Fu Chang
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Abstract

Current image captioning approaches generate descriptions which lack specific information, such as named entities that are involved in the images. In this paper we propose a new task which aims to generate informative image captions, given images and hashtags as input. We propose a simple but effective approach to tackle this problem. We first train a convolutional neural networks - long short term memory networks (CNN-LSTM) model to generate a template caption based on the input image. Then we use a knowledge graph based collective inference algorithm to fill in the template with specific named entities retrieved via the hashtags. Experiments on a new benchmark dataset collected from Flickr show that our model generates news-style image descriptions with much richer information. Our model outperforms unimodal baselines significantly with various evaluation metrics.

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Di Lu, Spencer Whitehead, Lifu Huang, Heng Ji, Shih-Fu Chang. 2018-11-07. Entity-aware Image Caption Generation. https://arxiv.org/abs/1804.07889

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