Object

Title: RNN with additional constant memory for image caption generation task

Co-author(s) :

Hakob Sarukhanyan

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.

Publisher:

RS Global Sp. z O.O.

Date submitted:

24.05.2017

Date accepted:

05.06.2017

Date of publication:

05.07.2017

Identifier:

oai:noad.sci.am:135945

ISSN:

2518-167X

Language:

English

Journal or Publication Title:

International academy journal WEB of scholar

Volume:

(4(13))

Number:

1

URL:

click here to follow the link

Affiliation:

Institute for Informatics and Automation Problems of NAS RA

Country:

Armenia

Indexing:

РИНЦ

Object collections:

Last modified:

May 3, 2021

In our library since:

Jul 27, 2020

Number of object content hits:

8

All available object's versions:

https://noad.sci.am/publication/149538

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