phi-LSTM: A Phrase-based Hierarchical LSTM Model for Image Captioning
This addresses the problem of generating more accurate and relevant image descriptions for applications in computer vision and AI, representing an incremental improvement over existing methods.
The paper tackles image captioning by proposing a phrase-based hierarchical LSTM model that encodes sentences as sequences of phrases and words, showing better or competitive results on Flickr8k and Flickr30k datasets.
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.