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COMIC: Towards A Compact Image Captioning Model with Attention
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 be deployed on embedded system 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
embedding vocabulary size that is 39x - 99x smaller. The source code and models
are available at:
https://github.com/jiahuei/COMIC-Compact-Image-Captioning-with-AttentionComment: Added source code link and new results in Table
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