Skip to main content
SpringerLink
Log in

Multivariate Attention Network for Image Captioning

  • Conference paper
  • First Online:
Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11366))

Included in the following conference series:

  • 1893 Accesses

Abstract

Recently, attention mechanism has been used extensively in computer vision to deeper understand image through selectively local analysis. However, the existing methods apply attention mechanism individually, which leads to irrelevant or inaccurate words. To solve this problem, we propose a Multivariate Attention Network (MAN) for image captioning, which contains a content attention for identifying content information of objects, a position attention for locating positions of important patches, and a minutia attention for preserving fine-grained information of target objects. Furthermore, we also construct a Multivariate Residual Network (MRN) to integrate the more discriminative multimodal representation via modeling the projections and extracting relevant relations among visual information of different modalities. Our MAN is inspired by the latest achievements in neuroscience, and designed to mimic the treatment of visual information on human brain. Compared with previous methods, we apply diverse visual information and exploit several multimodal integration strategies, which can significantly improve the performance of our model. The experimental results show that our MAN model outperforms the state-of-the-art approaches on two benchmark datasets MS-COCO and Flickr30K.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M.: Bottom-up and top-down attention for image captioning and visual question answering. In: Computer Vision and Pattern Recognition, pp. 6077–6086 (2018)

    Google Scholar 

  2. Benyounes, H., Cadene, R., Cord, M., Thome, N.: Mutan: multimodal tucker fusion for visual question answering. In: International Conference on Computer Vision, pp. 2612–2620 (2017)

    Google Scholar 

  3. Chen, L., et al.: SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: Computer Vision and Pattern Recognition, pp. 5659–5667 (2017)

    Google Scholar 

  4. Denkowski, M., Lavie, A.: Meteor 1.3: automatic metric for reliable optimization and evaluation of machine translation systems. In: Proceedings of the Sixth Workshop on Statistical Machine Translation, pp. 85–91. Association for Computational Linguistics (2011)

    Google Scholar 

  5. Flick, C.: ROUGE: a package for automatic evaluation of summaries. In: The Workshop on Text Summarization Branches Out, p. 10 (2004)

    Google Scholar 

  6. 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)

    Article  Google Scholar 

  7. Gan, Z., et al.: Semantic compositional networks for visual captioning. In: Computer Vision and Pattern Recognition, pp. 5630–5639 (2017)

    Google Scholar 

  8. Graves, A.: Long short-term memory. In: Graves, A. (ed.) Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence, vol. 385, pp. 37–45. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24797-2_4

    Chapter  MATH  Google Scholar 

  9. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  10. Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: Computer Vision and Pattern Recognition, pp. 3128–3137 (2015)

    Google Scholar 

  11. Kim, J.H., et al.: Multimodal residual learning for visual QA. In: Advances in Neural Information Processing Systems, pp. 361–369 (2016)

    Google Scholar 

  12. Kim, J.H., On, K.W., Lim, W., Kim, J., Ha, J.W., Zhang, B.T.: Hadamard product for low-rank bilinear pooling. arXiv preprint arXiv:1610.04325 (2016)

  13. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  14. Liu, S., Zhu, Z., Ye, N., Guadarrama, S., Murphy, K.: Improved image captioning via policy gradient optimization of spider. In: International Conference on Computer Vision, pp. 873–881 (2017)

    Google Scholar 

  15. Lu, J., Xiong, C., Parikh, D., Socher, R.: Knowing when to look: adaptive attention via a visual sentinel for image captioning. In: Computer Vision and Pattern Recognition, pp. 375–383 (2017)

    Google Scholar 

  16. Lu, J., Yang, J., Batra, D., Parikh, D.: Neural baby talk. In: Computer Vision and Pattern Recognition, pp. 7219–7228 (2018)

    Google Scholar 

  17. Lu, Y., et al.: Revealing detail along the visual hierarchy: neural clustering preserves acuity from v1 to v4. Neuron 98(2), 417–428 (2018)

    Article  Google Scholar 

  18. Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., Yuille, A.: Deep captioning with multimodal recurrent neural networks (m-RNN). arXiv preprint arXiv:1412.6632 (2014)

  19. 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)

    Google Scholar 

  20. Ranzato, M., Chopra, S., Auli, M., Zaremba, W.: Sequence level training with recurrent neural networks. arXiv preprint arXiv:1511.06732 (2015)

  21. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: International Conference on Neural Information Processing Systems, pp. 91–99 (2015)

    Google Scholar 

  22. Rennie, S.J., Marcheret, E., Mroueh, Y., Ross, J., Goel, V.: Self-critical sequence training for image captioning. In: Computer Vision and Pattern Recognition, pp. 7008–7024 (2017)

    Google Scholar 

  23. Ungerleider, L.G., Haxby, J.V.: ‘what’ and ‘where’ in the human brain. Curr. Opin. Neurobiol. 4(2), 157–165 (1994)

    Article  Google Scholar 

  24. Vedantam, R., Zitnick, C.L., Parikh, D.: CIDEr: consensus-based image description evaluation. In: Computer Vision and Pattern Recognition, pp. 4566–4575 (2015)

    Google Scholar 

  25. Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Li, F.F., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vis. 123(1), 32–73 (2017)

    Article  MathSciNet  Google Scholar 

  26. Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: Computer Vision and Pattern Recognition, pp. 3156–3164 (2015)

    Google Scholar 

  27. Wang, Y., Lin, Z., Shen, X., Cohen, S., Cottrell, G.W.: Skeleton key: image captioning by skeleton-attribute decomposition. In: Computer Vision and Pattern Recognition, pp. 7272–7281 (2017)

    Google Scholar 

  28. Wu, Q., Shen, C., Liu, L., Dick, A., Hengel, A.V.D.: What value do explicit high level concepts have in vision to language problems? In: Computer Vision and Pattern Recognition, pp. 203–212 (2016)

    Google Scholar 

  29. Xu, K., Ba, J.L., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., et al.: Show, attend and tell: neural image caption generation with visual attention. In: International Conference on Machine Learning, pp. 2048–2057 (2015)

    Google Scholar 

  30. You, Q., Jin, H., Wang, Z., Fang, C., Luo, J.: Image captioning with semantic attention. In: Computer Vision and Pattern Recognition, pp. 4651–4659 (2016)

    Google Scholar 

  31. Young, P., Lai, A., Hodosh, M., Hockenmaier, J.: From image descriptions to visual denotations: new similarity metrics for semantic inference over event descriptions. Trans. Assoc. Comput. Linguist. 2, 67–78 (2014)

    Article  Google Scholar 

  32. Zhang, L., et al.: Actor-critic sequence training for image captioning. arXiv preprint arXiv:1706.09601 (2017)

Download references

Acknowledgement

This work was supported in part by the NSFC (61673402), the NSF of Guangdong Province (2017A030311029), the Science and Technology Program of Guangzhou (201704020180), and the Fundamental Research Funds for the Central Universities of China.

Author information

Authors and Affiliations

  1. School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Weixuan Wang, Zhihong Chen & Haifeng Hu

Authors
  1. Weixuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhihong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haifeng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haifeng Hu .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, W., Chen, Z., Hu, H. (2019). Multivariate Attention Network for Image Captioning. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11366. Springer, Cham. https://doi.org/10.1007/978-3-030-20876-9_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20876-9_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20875-2

  • Online ISBN: 978-3-030-20876-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation