# phi-LSTM: A Phrase-based Hierarchical LSTM Model for Image Captioning

arXiv: https://arxiv.org/abs/1608.05813 · HTML: https://mlsanity.com/paper/1608.05813/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2016.

## Built on by (most cited first, 1 of 1)

- [DeepVARMA: A Hybrid Deep Learning and VARMA Model for Chemical Industry Index Forecasting](https://mlsanity.com/paper/2404.17615/) (2024)

## Abstract (arXiv)

A picture is worth a thousand words. Not until recently, however, we noticed some success stories in understanding of visual scenes: a model that is able to detect/name objects, describe their attributes, and recognize their relationships/interactions. In this paper, we propose a phrase-based hierarchical Long Short-Term Memory (phi-LSTM) model to generate image description. The proposed model encodes sentence as a sequence of combination of phrases and words, instead of a sequence of words alone as in those conventional solutions. The two levels of this model are dedicated to i) learn to generate image relevant noun phrases, and ii) produce appropriate image description from the phrases and other words in the corpus. Adopting a convolutional neural network to learn image features and the LSTM to learn the word sequence in a sentence, the proposed model has shown better or competitive results in comparison to the state-of-the-art models on Flickr8k and Flickr30k datasets.