EXplainable AI (XAI) approach to image captioning

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This article presents an eXplainable AI (XAI) approach to image captioning. Recently, deep learning techniques have been intensively used to this task with relatively good performance. Due to the 'black-box' paradigm of deep learning, however, existing approaches are unable to provide clues to explain the reasons why specific words have been selected when generating captions for given images, hence leading to generate absurd captions occasionally. To overcome this problem, this article proposes an explainable image captioning model, which provides a visual link between the region of an object (or a concept) in the given image and the particular word (or phrase) in the generated sentence. The model has been evaluated with two datasets, MSCOCO and Flickr30K, and both quantitative and qualitative results are presented to show the effectiveness of the proposed model.
Publisher
INST ENGINEERING TECHNOLOGY-IET
Issue Date
2020-07
Language
English
Article Type
Article; Proceedings Paper
Citation

JOURNAL OF ENGINEERING-JOE, v.2020, no.13, pp.589 - 594

ISSN
2051-3305
DOI
10.1049/joe.2019.1217
URI
http://hdl.handle.net/10203/276483
Appears in Collection
CS-Journal Papers(저널논문)
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