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

TL;DR

This paper introduces phi-LSTM, a hierarchical model that generates image descriptions by encoding sentences as phrases and words, improving captioning accuracy over existing methods on standard datasets.

Contribution

The paper presents a novel phrase-based hierarchical LSTM model that encodes and generates image captions using phrase-level and word-level information, enhancing caption quality.

Findings

Achieved better or competitive results on Flickr8k and Flickr30k datasets.

Demonstrated that phrase-based hierarchical encoding improves image captioning.

Outperformed some state-of-the-art models in image description tasks.

Abstract

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.