DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Dong-Jin | ko |
dc.contributor.author | Choi, Jinsoo | ko |
dc.contributor.author | Oh, Tae-Hyun | ko |
dc.contributor.author | Kweon, In-So | ko |
dc.date.accessioned | 2023-08-31T06:04:12Z | - |
dc.date.available | 2023-08-31T06:04:12Z | - |
dc.date.created | 2023-06-08 | - |
dc.date.issued | 2019-11 | - |
dc.identifier.citation | Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, pp.2012 - 2023 | - |
dc.identifier.uri | http://hdl.handle.net/10203/312063 | - |
dc.description.abstract | Constructing an organized dataset comprised of a large number of images and several captions for each image is a laborious task, which requires vast human effort. On the other hand, collecting a large number of images and sentences separately may be immensely easier. In this paper, we develop a novel dataefficient semi-supervised framework for training an image captioning model. We leverage massive unpaired image and caption data by learning to associate them. To this end, our proposed semi-supervised learning method assigns pseudo-labels to unpaired samples via Generative Adversarial Networks to learn the joint distribution of image and caption. To evaluate, we construct scarcely-paired COCO dataset, a modified version of MS COCO caption dataset. The empirical results show the effectiveness of our method compared to several strong baselines, especially when the amount of the paired samples are scarce. | - |
dc.language | English | - |
dc.publisher | Association for Computational Linguistics | - |
dc.title | Image captioning with very scarce supervised data: Adversarial semi-supervised learning approach | - |
dc.type | Conference | - |
dc.identifier.wosid | 000854193302016 | - |
dc.identifier.scopusid | 2-s2.0-85084292557 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 2012 | - |
dc.citation.endingpage | 2023 | - |
dc.citation.publicationname | Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019 | - |
dc.identifier.conferencecountry | CC | - |
dc.identifier.conferencelocation | Hong Kong | - |
dc.contributor.localauthor | Kweon, In-So | - |
dc.contributor.nonIdAuthor | Kim, Dong-Jin | - |
dc.contributor.nonIdAuthor | Choi, Jinsoo | - |
dc.contributor.nonIdAuthor | Oh, Tae-Hyun | - |
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