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• W3100183449 abstract "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." @default.
• W3100183449 created "2020-11-23" @default.
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• W3100183449 date "2019-10-01" @default.
• W3100183449 modified "2023-10-17" @default.
• W3100183449 title "COMIC: Toward A Compact Image Captioning Model With Attention" @default.
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• W3100183449 doi "https://doi.org/10.1109/tmm.2019.2904878" @default.
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