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Improving the Quality of Video-to-Language Models by Optimizing Annotation of the Training Material

  • Laura Pérez-Mayos
  • Federico M. Sukno
  • Leo Wanner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10704)

Abstract

Automatic video captioning is one of the ultimate challenges of Natural Language Processing, boosted by the omnipresence of video and the release of large-scale annotated video benchmarks. However, the specificity and quality of the captions vary considerably, having an adverse effect on the quality of the trained captioning models. In this work, we address this issue by proposing automatic strategies for optimizing the annotations of video material, removing annotations that are not semantically relevant and generating new and more informative captions. We evaluate our approach on the MSR-VTT challenge with a state-of-the-art deep learning video-to-language model. Our code is available at https://github.com/lpmayos/mcv_thesis.

Keywords

Video-to-language Video captioning Video understanding Text annotation optimization Semantic sentence similarity 

Notes

Acknowledgment

This work is partly supported by the Spanish Ministry of Economy and Competitiveness under the Ramon y Cajal fellowships, and the Kristina project funded by the European Union Horizon 2020 research and innovation programme under grant agreement No 645012. The Titan X GPU used for this research was donated by the NVIDIA Corporation.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Laura Pérez-Mayos
    • 1
  • Federico M. Sukno
    • 1
  • Leo Wanner
    • 1
    • 2
  1. 1.Department of Information and Communication TechnologiesPompeu Fabra UniversityBarcelonaSpain
  2. 2.Catalan Institute for Research and Advanced Studies (ICREA)BarcelonaSpain

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