# SuperCaptioning: Image Captioning Using Two-dimensional Word Embedding

arXiv: https://arxiv.org/abs/1905.10515 · HTML: https://mlsanity.com/paper/1905.10515/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2019.

## Built on by (most cited first, 2 of 2)

- [GnetSeg: Semantic Segmentation Model Optimized on a 224mW CNN Accelerator Chip at the Speed of 318FPS](https://mlsanity.com/paper/2101.10444/) (2021)
- [GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS](https://mlsanity.com/paper/2103.15756/) (2021)

## Abstract (arXiv)

Language and vision are processed as two different modal in current work for image captioning. However, recent work on Super Characters method shows the effectiveness of two-dimensional word embedding, which converts text classification problem into image classification problem. In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model. The experimental results on Flickr30k data shows the proposed method gives high quality image captions. An interactive demo is ready to show at the workshop.