# Visual Storytelling via Predicting Anchor Word Embeddings in the Stories

arXiv: https://arxiv.org/abs/2001.04541 · HTML: https://mlsanity.com/paper/2001.04541/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

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

- [Ordered Attention for Coherent Visual Storytelling](https://mlsanity.com/paper/2108.02180/) (2021)
- [Semantic Frame Forecast](https://mlsanity.com/paper/2104.05604/) (2021)

## Abstract (arXiv)

We propose a learning model for the task of visual storytelling. The main idea is to predict anchor word embeddings from the images and use the embeddings and the image features jointly to generate narrative sentences. We use the embeddings of randomly sampled nouns from the groundtruth stories as the target anchor word embeddings to learn the predictor. To narrate a sequence of images, we use the predicted anchor word embeddings and the image features as the joint input to a seq2seq model. As opposed to state-of-the-art methods, the proposed model is simple in design, easy to optimize, and attains the best results in most automatic evaluation metrics. In human evaluation, the method also outperforms competing methods.