3M: Multi-style image caption generation using Multi-modality features under Multi-UPDOWN model

Abstract

In this paper, we build a multi-style generative model for stylish image captioning which uses multimodality image features, ResNeXt features, and text features generated by DenseCap. We propose the 3M model, a Multi-UPDOWN caption model that encodes multi-modality features and decodes them into captions. We demonstrate the effectiveness of our model on generating human-like captions by examining its performance on two datasets, the PERSONALITYCAPTIONS dataset, and the FlickrStyle10K dataset. We compare against a variety of state-of-the-art baselines on various automatic NLP metrics such as BLEU, ROUGE-L, CIDEr, SPICE, etc1 . A qualitative study has also been done to verify our 3M model can be used for generating different stylized captions.