Integrating Part of Speech Guidance for Image Captioning

To generate an image caption, firstly, the content of the image should be fully understood; and then the semantic information contained in the image should be described using a phrase or statement that conforms to certain grammatical rules. Thus, it requires techniques from both computer vision and natural language processing to connect the two different media forms together, which is highly challenging. To adaptively adjust the effect of visual information and language information on the captioning process, in this paper, the part of speech information is proposed to novelly integrate with image captioning models based on the encoder-decoder framework. First, a part of speech prediction network is proposed to analyze and model the part of speech sequences for the words in natural language sentences; then, different mechanisms are proposed to integrate the part of speech guidance information with merge-based and inject-based image captioning models, respectively; finally, according to the integrated frameworks, a multi-task learning paradigm is proposed to facilitate model training. Experiments are conducted on two widely used image captioning datasets, Flickr30 k and COCO, and the results have validated that the image captions generated by the proposed method contain more accurate visual information and comply with language habits and grammar rules better.