Advertisement

Exploring the Impact of Training Data Bias on Automatic Generation of Video Captions

  • Alan F. SmeatonEmail author
  • Yvette Graham
  • Kevin McGuinness
  • Noel E. O’Connor
  • Seán Quinn
  • Eric Arazo Sanchez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)

Abstract

A major issue in machine learning is availability of training data. While this historically referred to the availability of a sufficient volume of training data, recently this has shifted to the availability of sufficient unbiased training data. In this paper we focus on the effect of training data bias on an emerging multimedia application, the automatic captioning of short video clips. We use subsets of the same training data to generate different models for video captioning using the same machine learning technique and we evaluate the performances of different training data subsets using a well-known video caption benchmark, TRECVid. We train using the MSR-VTT video-caption pairs and we prune this to reduce and make the set of captions describing a video more homogeneously similar, or more diverse, or we prune randomly. We then assess the effectiveness of caption-generating trained with these variations using automatic metrics as well as direct assessment by human assessors. Our findings are preliminary and show that randomly pruning captions from the training data yields the worst performance and that pruning to make the data more homogeneous, or diverse, does improve performance slightly when compared to random. Our work points to the need for more training data, both more video clips but, more importantly, more captions for those videos.

Keywords

Video-to-language Video captioning Video understanding Semantic similarity 

Notes

Acknowledgements

This work is supported by Science Foundation Ireland under grant numbers 12/RC/2289 and 15/SIRG/3283.

References

  1. 1.
    Aafaq, N., Gilani, S.Z., Liu, W., Mian, A.: Video description: a survey of methods, datasets and evaluation metrics. arXiv preprint arXiv:1806.00186 (2018)
  2. 2.
    Aneja, J., Deshpande, A., Schwing, A.G.: Convolutional image captioning. In: Computer Vision and Pattern Recognition (CVPR), June 2018Google Scholar
  3. 3.
    Awad, G., et al.: TRECVID 2017: evaluating ad-hoc and instance video search, events detection, video captioning and hyperlinking. In: Proceedings of TRECVID 2017. NIST (2017)Google Scholar
  4. 4.
    Baeza-Yates, R.: Bias on the web. Commun. ACM 61(6), 54–61 (2018)CrossRefGoogle Scholar
  5. 5.
    Baltrušaitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. (Early Access) (2018).  https://doi.org/10.1109/TPAMI.2018.2798607CrossRefGoogle Scholar
  6. 6.
    Chen, X., et al.: Microsoft COCO captions: data collection and evaluation server. CoRR, abs/1504.00325 (2015)Google Scholar
  7. 7.
    Graham, Y., Awad, G., Smeaton, A.: Evaluation of automatic video captioning using direct assessment. CoRR, abs/1710.10586 (2017)Google Scholar
  8. 8.
    Graham, Y., Mathur, N., Baldwin, T.: Randomized significance tests in machine translation. In: ACL 2014 Workshop on Statistical Machine Translation, pp. 266–274. Association for Computational Linguistics (2014)Google Scholar
  9. 9.
    Han, L., Kashyap, A., Finin, T., Mayfield, J., Weese, J.: UMBC EBIQUITY-CORE: semantic textual similarity systems. Joint. Conf. Lex. Comput. Semant. 1, 44–52 (2013)Google Scholar
  10. 10.
    Iacobacci, I., Pilehvar, M.T., Navigli, R.: SensEmbed: learning sense embeddings for word and relational similarity. In: Proceedings of ACL, pp. 95–105 (2015)Google Scholar
  11. 11.
    Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Karpathy, A.: Connecting images and natural language. Ph.D. thesis, Stanford University, August 2016Google Scholar
  13. 13.
    Kashyap, A., et al.: Robust semantic text similarity using LSA, machine learning, and linguistic resources. Lang. Resour. Eval. 50(1), 125–161 (2016)CrossRefGoogle Scholar
  14. 14.
    Kilickaya, M., Erdem, A., Ikizler-Cinbis, N., Erdem, E.: Re-evaluating automatic metrics for image captioning. In: Proceedings of EACL, April 2017Google Scholar
  15. 15.
    Marsden, M., et al.: Dublin City University and partners’ participation in the INS and VTT tracks at TRECVid 2016. In: Proceedings of TREVid, NIST, Gaithersburg, MD, USA (2016)Google Scholar
  16. 16.
    Pan, Y., Mei, T., Yao, T., Li, H., Rui, Y.: Jointly modeling embedding and translation to bridge video and language. In: Computer Vision and Pattern Recognition (CVPR), pp. 4594–4602 (2016)Google Scholar
  17. 17.
    Pérez-Mayos, L., Sukno, F.M., Wanner, L.: Improving the quality of video-to-language models by optimizing annotation of the training material. In: Schoeffmann, K., et al. (eds.) MMM 2018. LNCS, vol. 10704, pp. 279–290. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-73603-7_23CrossRefGoogle Scholar
  18. 18.
    Smeaton, A.F., Over, P., Kraaij, W.: Evaluation campaigns and TRECVid. In: MIR 2006: International Workshop on Multimedia Information Retrieval, pp. 321–330 (2006)Google Scholar
  19. 19.
    Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., Saenko, K.: Sequence to sequence – video to text. In: International Conference on Computer Vision (ICCV) (2015)Google Scholar
  20. 20.
    Xu, J., Mei, T., Yao, T., Rui, Y.: MSR-VTT: a large video description dataset for bridging video and language. In: Computer Vision and Pattern Recognition (CVPR), pp. 5288–5296, June 2016Google Scholar
  21. 21.
    Zhou, L., Zhou, Y., Corso, J.J., Socher, R., Xiong, C.: End-to-end dense video captioning with masked transformer. In: Computer Vision and Pattern Recognition (CVPR), June 2018Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alan F. Smeaton
    • 1
    Email author
  • Yvette Graham
    • 1
  • Kevin McGuinness
    • 1
  • Noel E. O’Connor
    • 1
  • Seán Quinn
    • 1
  • Eric Arazo Sanchez
    • 1
  1. 1.Insight Centre for Data AnalyticsDublin City UniversityDublin 9Ireland

Personalised recommendations