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

• URL: http://arxiv.org/abs/2103.11186v1
• Date: Sat, 20 Mar 2021 14:12:13 GMT
• Title: 3M: Multi-style image caption generation using Multi-modality features under Multi-UPDOWN model
• Authors: Chengxi Li and Brent Harrison
• Abstract summary: We propose the 3M model, a Multi-UPDOWN caption model that encodes multi-modality features and decodes them to captions. We demonstrate the effectiveness of our model on generating human-like captions by examining its performance on two datasets.
• Score: 8.069209836624495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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