Abstract
Most of the existing image captioning methods only use the visual information of the image to guide the generation of the captions, lack the guidance of effective scene semantic information, and the current visual attention mechanism cannot adjust the focus intensity on the image. In this paper, we first propose an improved visual attention model. At each time step, we calculate the focus intensity coefficient of the attention mechanism through the context information of the model, and automatically adjust the focus intensity of the attention mechanism through the coefficient, so as to extract more accurate image visual information. In addition, we represent the scene semantic information of the image through some topic words related to the image scene, and add them to the language model. We use attention mechanism to determine the image visual information and scene semantic information that the model pays attention to at each time step, and combine them to guide the model to generate more accurate and scene-specific captions. Finally, we evaluate our model on MSCOCO dataset. The experimental results show that our approach can generate more accurate captions, and outperforms many recent advanced models on various evaluation metrics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anderson, P., et al.: Bottom-up and top-down attention for image captioning and visual question answering. In: Proceedings CVPR, pp. 6077–6086 (2018)
Banerjee, S., Lavie, A.: Meteor: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings ACL, pp. 65–72 (2005)
Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Proceedings NIPS, pp. 379–387 (2016)
Fang, H., et al.: From captions to visual concepts and back. In: Proceedings CVPR, pp. 1473–1482 (2015)
Fu, K., Jin, J., Cui, R., Sha, F., Zhang, C.: Aligning where to see and what to tell: Image captioning with region-based attention and scene-specific contexts. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2321–2334 (2017)
Gu, J., Cai, J., Wang, G., Chen, T.: Stack-captioning: coarse-to-fine learning for image captioning. In: Proceedings AAAI (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings CVPR, pp. 770–778 (2016)
Jiang, W., Ma, L., Jiang, Y.G., Liu, W., Zhang, T.: Recurrent fusion network for image captioning. In: Proceedings ECCV, pp. 499–515 (2018)
Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: Proceedings CVPR, pp. 3128–3137 (2015)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint (2014). arXiv:1412.6980
Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. Text Summarization Branches Out, pp. 74–81 (2004)
Liu, D., Zha, Z.J., Zhang, H., Zhang, Y., Wu, F.: Context-aware visual policy network for sequence-level image captioning. arXiv preprint (2018). arXiv:1808.05864
Lu, J., Xiong, C., Parikh, D., Socher, R.: Knowing when to look: adaptive attention via a visual sentinel for image captioning. In: Proceedings CVPR, pp. 375–383 (2017)
Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., Yuille, A.: Deep captioning with multimodal recurrent neural networks (m-rnn). arXiv preprint (2014). arXiv:1412.6632
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. Association for Computational Linguistics (2002)
Rennie, S.J., Marcheret, E., Mroueh, Y., Ross, J., Goel, V.: Self-critical sequence training for image captioning. In: Proceedings CVPR, pp. 7008–7024 (2017)
Shuster, K., Humeau, S., Hu, H., Bordes, A., Weston, J.: Engaging image captioning via personality. In: Proceedings CVPR, pp. 12516–12526 (2019)
Vedantam, R., Lawrence Zitnick, C., Parikh, D.: Cider: consensus-based image description evaluation. In: Proceedings CVPR, pp. 4566–4575 (2015)
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: Proceedings CVPR, pp. 3156–3164 (2015)
Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: Proceedings ICML, pp. 2048–2057 (2015)
You, Q., Jin, H., Wang, Z., Fang, C., Luo, J.: Image captioning with semantic attention. In: Proceedings CVPR, pp. 4651–4659 (2016)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Nos. 61966004, 61663004, 61762078, 61866004), the Guangxi Natural Science Foundation (Nos. 2016GXNSFAA380146, 2017GXNSFAA198365, 2018GXNSFDA281009), the Research Fund of Guangxi Key Lab of Multi-source Information Mining and Security (16-A-03-02, MIMS18-08), the Guangxi Special Project of Science and Technology Base and Talents (AD16380008), Innovation Project of Guangxi Graduate Education(XYCSZ2019068), the Guangxi “Bagui Scholar” Teams for Innovation and Research Project, Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wei, H., Li, Z., Zhang, C. (2020). Image Captioning Based on Visual and Semantic Attention. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-37731-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37730-4
Online ISBN: 978-3-030-37731-1
eBook Packages: Computer ScienceComputer Science (R0)