Interpretable Image Caption Generation Based on Dependency Syntax

Liu Maofu; Bi Jianqi; Zhou Bingying; Hu Huijun

doi:10.7544/issn1000-1239.202220432

Journal of Computer Research and Development > 2023 > 60(9): 2115-2126. > DOI: 10.7544/issn1000-1239.202220432

Liu Maofu, Bi Jianqi, Zhou Bingying, Hu Huijun. Interpretable Image Caption Generation Based on Dependency Syntax[J]. Journal of Computer Research and Development, 2023, 60(9): 2115-2126. DOI: 10.7544/issn1000-1239.202220432

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Interpretable Image Caption Generation Based on Dependency Syntax

School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065

Funds: This work was supported by the Major Projects of the National Social Science Foundation of China (11&ZD189), the Planning Projects of Guizhou Provincial Science and Technology ([2020]3003), and the Graduate Innovation Fund of Wuhan University of Science and Technology (2022210).

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Author Bio:
Liu Maofu: born in 1977. PhD, professor. Senior member of CCF. His main research interests include natural language processing and intelligent media computing

Bi Jianqi: born in 1998. MSc. His main research interest includes intelligent media computing

Zhou Bingying: born in 2000. Master candidate. Her main research interest includes intelligent media computing

Hu Huijun: born in 1976. PhD, associate professor. Her main research interests include natural language processing and intelligent media computing
Received Date: May 31, 2022
Revised Date: September 26, 2022
Available Online: April 13, 2023

Graphical Abstract

Abstract

Abstract

Although existing image captioning models can detect and represent target objects and visual relationships, they have not focused on the interpretability of image captioning model from the perspective of syntactic relations. To this end, we present an interpretable image caption generation based on dependency syntax triplets modeling (IDSTM) , which leverages the multi-task learning to jointly generate the dependency syntax triplet sequence and image caption. IDSTM firstly obtains the potential dependency syntactic features from the input image through the dependency syntax encoder, and then incorporates these features with the dependency syntactic triplets and textual word embedding vectors into single LSTM (long short-term memory) to generate the dependency syntactic triplet sequence as the prior knowledge. Secondly, the dependency syntactic features are input into captioning encoder to extract the visual object textual features. Finally, hard and soft limitations are adopted to employ the dependency syntactic and relation features into double LSTM for the interpretable image caption generation. With the generation of the task of dependency syntax triplet sequence, IDSTM improves the interpretability of the image captioning model without a significant decrease on the accuracy of the generated captions. In addition, we propose novel metrics B1-DS (BLEU-1-DS), B4-DS (BLEU-4-DS) and M-DS (METEOR-DS) to testify the quality of dependency syntax triplets and to show extensive experimental results on MSCOCO dataset for evaluating the effectiveness and interpretability of IDSTM.
- image caption generation,
- dependency syntax,
- interpretability,
- multi-task learning

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References (38)

References

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