COMIC: Toward A Compact Image Captioning Model With Attention

Tan, Jia Huei and Chan, Chee Seng and Chuah, Joon Huang (2019) COMIC: Toward A Compact Image Captioning Model With Attention. IEEE Transactions on Multimedia, 21 (10). pp. 2686-2696. ISSN 1520-9210, DOI

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Recent works in image captioning have shown very promising raw performance. However, we realize that most of these encoder-decoder style networks with attention do not scale naturally to large vocabulary size, making them difficult to deploy on embedded systems with limited hardware resources. This is because the size of word and output embedding matrices grow proportionally with the size of vocabulary, adversely affecting the compactness of these networks. To address this limitation, this paper introduces a brand new idea in the domain of image captioning. That is, we tackle the problem of compactness of image captioning models which is hitherto unexplored. We showed that our proposed model, named COMIC for compact image captioning, achieves comparable results in five common evaluation metrics with state-of-the-art approaches on both MS-COCO and InstaPIC-1.1M datasets despite having an embedded vocabulary size that is 39×-99× smaller. © 1999-2012 IEEE.

Item Type: Article
Funders: UM Frontier Research under Grant FG002-17AFR, from the University of Malaya
Uncontrolled Keywords: deep compression network; deep learning; Image captioning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Computer Science & Information Technology
Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 06 Jan 2020 01:50
Last Modified: 06 Jan 2020 01:50

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