RNN WITH ADDITIONAL CONSTANT MEMORY FOR IMAGE CAPTION GENERATION TASK
Journal Title: INTERNATIONAL ACADEMY JOURNAL "Web of Scholar" - Year 2017, Vol 1, Issue 4
Abstract
Analyze and generation of automated captions for images is one of the most common problems in artificial intelligence. Existing works use LSTM (Long Short-Term Memory) as recurrent neural network cell to solve this task. After training their deep neural models can generate image caption. But there is an issue, the next predicted word of the caption depends mainly on the last predicted word, rather than image content. In this paper, we present model that can automatically generate image description and is based on a recurrent neural network with modified LSTM cell that has an additional gate responsible for image features. This modification results in generation of more accurate captions. We have trained and tested our model on MSCOCO image dataset by using only images and their captions.
Authors and Affiliations
Aghasi Poghosyan, Hakob Sarukhanyan
Keywords
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