COMIC: Towards A Compact Image Captioning Model with Attention
This addresses the compactness issue for deploying image captioning models on resource-constrained devices, presenting a novel approach in an underexplored area.
The paper tackles the problem of scaling image captioning models to large vocabularies, which increases model size and hinders deployment on embedded systems, and shows that their COMIC model achieves comparable results on MS-COCO and InstaPIC-1.1M datasets with 39x to 99x smaller embedding vocabulary.
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-Attention