  In this paper, we build a multi-style generative model for stylish image
captioning which uses multi-modality 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 decode them to captions. We
demonstrate the effectiveness of our model on generating human-like captions by
examining its performance on two datasets, the PERSONALITY-CAPTIONS 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,
etc. A qualitative study has also been done to verify our 3M model can be used
for generating different stylized captions.
