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Attention based video captioning framework for Hindi

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A Correction to this article was published on 17 July 2021

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Abstract

In recent times, active research is going on for bridging the gap between computer vision and natural language. In this paper, we attempt to address the problem of Hindi video captioning. In a linguistically diverse country like India, it is important to provide a means which can help in understanding the visual entities in native languages. In this work, we employ a hybrid attention mechanism by extending the soft temporal attention mechanism with a semantic attention to make the system able to decide when to focus on visual context vector and semantic input. The visual context vector of the input video is extracted using 3D convolutional neural network (3D CNN) and a Long Short-Term Memory (LSTM) recurrent network with attention module is used for decoding the encoded context vector. We experimented on a dataset built in-house for Hindi video captioning by translating \(MSR-VTT\) dataset followed by post-editing. Our system achieves 0.369 CIDEr score and 0.393 METEOR score and outperformed other baseline models including RMN (Reasoning Module Networks)-based model.

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Notes

  1. [ ; ] indicates the concatenation.

  2. https://github.com/alokssingh/MSR-VTT-captioning.

  3. http://ms-multimedia-challenge.com/2017/challenge.

  4. https://anoopkunchukuttan.github.io/indic_nlp_library/.

  5. https://github.com/anoopkunchukuttan/meteor_indic.

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

  7. http://www.cfilt.iitb.ac.in/.

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Acknowledgements

This work is supported by Scheme for Promotion of Academic and Research Collaboration (SPARC) Project Code: P995 of No: SPARC/2018-2019/119/SL (IN) under MHRD, Govt of India.

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Authors and Affiliations

  1. Center for Natural Language Processing (CNLP) NIT, Silchar, Assam, India

    Alok Singh, Thoudam Doren Singh & Sivaji Bandyopadhyay

  2. Department of Computer Science and Engineering, National Institute of Technology, Silchar, Assam, India

    Alok Singh, Thoudam Doren Singh & Sivaji Bandyopadhyay

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  1. Alok Singh
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  2. Thoudam Doren Singh
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Correspondence to Alok Singh.

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Communicated by T. Yao.

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Singh, A., Singh, T.D. & Bandyopadhyay, S. Attention based video captioning framework for Hindi. Multimedia Systems 28, 195–207 (2022). https://doi.org/10.1007/s00530-021-00816-3

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