Multi-guiding long short-term memory for video captioning

Abstract

Recently, research interests have been paid for using recurrent neural network (RNN) as the decoder in video captioning task. However, the generated sentence seems to “lose track” of the video content due to the fixed language rule. Though existing methods try to “guide” the decoder and keep it “on track”, they mainly rely on a single-modal feature that does not fit the multi-modal (visual and semantic) and the complementary (local and global) nature of the video captioning task. To this end, we propose the multi-guiding long short-term memory (mg-LSTM), an extension of LSTM network for video captioning. We add global information (i.e., detected attributes) and local information (i.e., appearance features) extracted from the video as extra input to each cell of LSTM, with the aim of collaboratively guiding the model towards solutions that are more tightly coupled to the video content. In particular, the appearance and attribute features are first used to produce local and global guiders, respectively. We propose a novel cell-wise ensemble, where the weight matrix of each cell of LSTM is extended to be a set of attribute-dependent and attention-dependent weight matrices, by which the guiders induce each cell optimization over time. Extensive experiments on three benchmark datasets (i.e., MSVD, MSR-VTT, and MPII-MD) show that our method can achieve competitive results against the state of the art. Additional ablation studies are conducted on variants of the proposed mg-LSTM.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Notes

  1. 1.

    https://github.com/tylin/coco-caption.

References

  1. 1.

    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: ICLR (2015)

  2. 2.

    Baraldi, L., Grana, C., Cucchiara, R.: Hierarchical boundary-aware neural encoder for video captioning. In: CVPR, pp. 1657–1666 (2017)

  3. 3.

    Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM TIST 2(3), 27:1–27:27 (2011)

    Google Scholar 

  4. 4.

    Chen, D., Dolan, W.B.: Collecting highly parallel data for paraphrase evaluation. In: ACL, pp. 190–200 (2011)

  5. 5.

    Denkowski, M.J., Lavie, A.: Meteor universal: language specific translation evaluation for any target language. In: WMT@ACL, pp. 376–380 (2014)

  6. 6.

    Dong, J., Li, X., Lan, W., Huo, Y., Snoek, C.G.M.: Early embedding and late reranking for video captioning. In: ACMMM, pp. 1082–1086 (2016)

  7. 7.

    Gan, Z., Gan, C., He, X., Pu, Y., Tran, K., Gao, J., Carin, L., Deng, L.: Semantic compositional networks for visual captioning. In: CVPR, pp. 1141–1150 (2017)

  8. 8.

    Gao, L., Guo, Z., Zhang, H., Xu, X., Shen, H.T.: Video captioning with attention-based LSTM and semantic consistency. IEEE Trans. Multimedia 19(9), 2045–2055 (2017)

    Article  Google Scholar 

  9. 9.

    Guadarrama, S., Krishnamoorthy, N., Malkarnenkar, G., Venugopalan, S., Mooney, R.J., Darrell, T., Saenko, K.: Youtube2text: Recognizing and describing arbitrary activities using semantic hierarchies and zero-shot recognition. In: ICCV, pp. 2712–2719 (2013)

  10. 10.

    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

  11. 11.

    Hori, C., Hori, T., Lee, T.Y., Zhang, Z., Harsham, B., Hershey, J.R., Marks, T.K., Sumi, K.: Attention-based multimodal fusion for video description. In: ICCV, pp. 4193–4202 (2017)

  12. 12.

    Jia, X., Gavves, E., Fernando, B., Tuytelaars, T.: Guiding the long-short term memory model for image caption generation. In: ICCV, pp. 2407–2415 (2015)

  13. 13.

    Jin, Q., Chen, J., Chen, S., Xiong, Y., Hauptmann, A.G.: Describing videos using multi-modal fusion. In: ACMM MM, pp. 1087–1091 (2016)

  14. 14.

    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2015)

  15. 15.

    Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: ACL Workshop, pp. 74–81 (2004)

  16. 16.

    Liu, Z., Cheng, L., Liu, A., Zhang, L., He, X., Zimmermann, R.: Multiview and Multimodal Pervasive Indoor Localization. In: ACMMM, pp. 109-117 (2017)

  17. 17.

    Liu, A., Xu, N., Wong, Y., Li, J., Su, Y., Kankanhalli, M.S.: Hierarchical & multimodal video captioning: discovering and transferring multimodal knowledge for vision to language. CVIU 163, 113–125 (2017)

    Google Scholar 

  18. 18.

    Pan, P., Xu, Z., Yang, Y., Wu, F., Zhuang, Y.: Hierarchical recurrent neural encoder for video representation with application to captioning. In: CVPR, pp. 1029–1038 (2016)

  19. 19.

    Pan, Y., Mei, T., Yao, T., Li, H., Rui, Y.: Jointly modeling embedding and translation to bridge video and language. In: CVPR, pp. 4594–4602 (2016)

  20. 20.

    Pan, Y., Yao, T., Li, H., Mei, T.: Video captioning with transferred semantic attributes. In: CVPR, pp. 984–992 (2017)

  21. 21.

    Papineni, K., Roukos, S., Ward, T., Zhu, W.: Bleu: a method for automatic evaluation of machine translation. In: ACL, pp. 311–318 (2002)

  22. 22.

    Ramanishka, V., Das, A., Park, D.H., Venugopalan, S., Hendricks, L.A., Rohrbach, M., Saenko, K.: Multimodal video description. In: ACM MM, pp. 1092–1096 (2016)

  23. 23.

    Rohrbach, A., Rohrbach, M., Schiele, B.: The long-short story of movie description. In: GCPR, pp. 209–221 (2015)

  24. 24.

    Rohrbach, A., Rohrbach, M., Tandon, N., Schiele, B.: A dataset for movie description. In: CVPR, pp. 3202–3212 (2015)

  25. 25.

    Shen, Z., Li, J., Su, Z., Li, M., Chen, Y., Jiang, Y., Xue, X.: Weakly supervised dense video captioning. In: CVPR, pp. 1916–1924 (2017)

  26. 26.

    Shetty, R., Laaksonen, J.: Frame- and segment-level features and candidate pool evaluation for video caption generation. In: ACM MM, pp. 1073–1076 (2016)

  27. 27.

    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)

  28. 28.

    Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS, pp. 3104–3112 (2014)

  29. 29.

    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR, pp. 1–9 (2015)

  30. 30.

    Theano Development Team: Theano: A Python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688 (2016)

  31. 31.

    Thomason, J., Venugopalan, S., Guadarrama, S., Saenko, K., Mooney, R.J.: Integrating language and vision to generate natural language descriptions of videos in the wild. In: COLING, pp. 1218–1227 (2014)

  32. 32.

    Vedantam, R., Zitnick, C.L., Parikh, D.: Cider: Consensus-based image description evaluation. In: CVPR, pp. 4566–4575 (2015)

  33. 33.

    Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R.J., Darrell, T., Saenko, K.: Sequence to sequence - video to text. In: ICCV, pp. 4534–4542 (2015)

  34. 34.

    Venugopalan, S., Xu, H., Donahue, J., Rohrbach, M., Mooney, R.J., Saenko, K.: Translating videos to natural language using deep recurrent neural networks. In: HLT-NAACL, pp. 1494–1504 (2015)

  35. 35.

    Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: A neural image caption generator. In: CVPR, pp. 3156–3164 (2015)

  36. 36.

    Wu, Q., Shen, C., Liu, L., Dick, A.R., van den Hengel, A.: What value do explicit high level concepts have in vision to language problems? In: CVPR, pp. 203–212 (2016)

  37. 37.

    Xu, J., Mei, T., Yao, T., Rui, Y.: MSR-VTT: A large video description dataset for bridging video and language. In: CVPR, pp. 5288–5296 (2016)

  38. 38.

    Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A.C., Salakhutdinov, R., Zemel, R.S., Bengio, Y.: Show, attend and tell: neural image caption generation with visual attention. In: ICML, pp. 2048–2057 (2015)

  39. 39.

    Xu, N., Liu, A., Wong, Y., Zhang, Y., Nie, W., Su, Y., Kankanhalli, M.: Dual-stream recurrent neural network for video captioning. IEEE Trans. Circuits Syst. Video Techn. (2018). https://doi.org/10.1109/TCSVT.2018.2867286

    Article  Google Scholar 

  40. 40.

    Yao, L., Torabi, A., Cho, K., Ballas, N., Pal, C.J., Larochelle, H., Courville, A.C.: Describing videos by exploiting temporal structure. In: ICCV, pp. 4507–4515 (2015)

  41. 41.

    Yu, H., Wang, J., Huang, Z., Yang, Y., Xu, W.: Video paragraph captioning using hierarchical recurrent neural networks. In: CVPR, pp. 4584–4593 (2016)

  42. 42.

    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: ECCV, pp. 818–833 (2014)

  43. 43.

    Zhang, X., Gao, K., Zhang, Y., Zhang, D., Tian, Q.: Task-driven dynamic fusion: Reducing ambiguity in video description. In: CVPR (2017)

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61772359, 61472275, 61502337).

Author information

Affiliations

Authors

Corresponding authors

Correspondence to An-An Liu or Weizhi Nie.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xu, N., Liu, A., Nie, W. et al. Multi-guiding long short-term memory for video captioning. Multimedia Systems 25, 663–672 (2019). https://doi.org/10.1007/s00530-018-0598-5

Download citation

Keywords

  • Long Short-term Memory (LSTM)
  • Video Capture
  • LSTM Network
  • Global Guidance
  • Learning Cell