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Where to put the image in an image caption generator

Published online by Cambridge University Press:  23 April 2018

MARC TANTI ,
ALBERT GATT  and
KENNETH P. CAMILLERI Open the ORCID record for KENNETH P. CAMILLERI [Opens in a new window]
MARC TANTI
Affiliation:
Institute of Linguistics and Language Technology, University of Malta, Msida MSD, Malta e-mail: marc.tanti.06@um.edu.mt, albert.gatt@um.edu.mt
ALBERT GATT
Affiliation:
Institute of Linguistics and Language Technology, University of Malta, Msida MSD, Malta e-mail: marc.tanti.06@um.edu.mt, albert.gatt@um.edu.mt
KENNETH P. CAMILLERI
Affiliation:
Department of Systems and Control Engineering, University of Malta, Msida MSD, Malta e-mail: kenneth.camilleri@um.edu.mt
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Abstract

When a recurrent neural network (RNN) language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in the RNN – conditioning the language model by ‘injecting’ image features – or in a layer following the RNN – conditioning the language model by ‘merging’ image features. While both options are attested in the literature, there is as yet no systematic comparison between the two. In this paper, we empirically show that it is not especially detrimental to performance whether one architecture is used or another. The merge architecture does have practical advantages, as conditioning by merging allows the RNN’s hidden state vector to shrink in size by up to four times. Our results suggest that the visual and linguistic modalities for caption generation need not be jointly encoded by the RNN as that yields large, memory-intensive models with few tangible advantages in performance; rather, the multimodal integration should be delayed to a subsequent stage.

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Articles
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Natural Language Engineering , Volume 24 , Special Issue 3: Language for Images , May 2018 , pp. 467 - 489
Copyright
Copyright © Cambridge University Press 2018 

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