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Hyperparameter Tuning over an Attention Model for Image Captioning

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Information and Communication Technologies (TICEC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1456))

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Abstract

Considering the historical trajectory and evolution of image captioning as a research area, this paper focuses on visual attention as an approach to solve captioning tasks with computer vision. This article studies the efficiency of different hyperparameter configurations on a state-of-the-art visual attention architecture composed of a pre-trained residual neural network encoder, and a long short-term memory decoder. Results show that the selection of both the cost function and the gradient-based optimizer have a significant impact on the captioning results. Our system considers the cross-entropy, Kullback-Leibler divergence, mean squared error, and the negative log-likelihood loss functions, as well as the adaptive momentum, AdamW, RMSprop, stochastic gradient descent, and Adadelta optimizers. Based on the performance metrics, a combination of cross-entropy with Adam is identified as the best alternative returning a Top-5 accuracy value of 73.092, and a BLEU-4 value of 0.201. Setting the cross-entropy as an independent variable, the first two optimization alternatives prove the best performance with a BLEU-4 metric value of 0.201. In terms of the inference loss, Adam outperforms AdamW with 3.413 over 3.418 and a Top-5 accuracy of 73.092 over 72.989.

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Acknowledgments

This research was partially supported by the Ecuadorian Corporation for the Development of Research and Academia (CEDIA), under the project “Divulga Ciencia 2021”, grant No.: JLE-CN-2021-0105. This research was partially supported by NVIDIA Corporation under the program “NVIDIA Jetson Nano 2GB Developer Kit”.

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Authors and Affiliations

  1. School of Mathematical and Computational Sciences, Deep Learning for Autonomous Driving, Robotics, and Computer Vision Research Group (DeepARC), Yachay Scientific Computing Group (SCG), Yachay Tech University, Hda. San José s/n y Proyecto Yachay, 100119, Urcuquí, Ecuador

    Roberto Castro, Israel Pineda & Manuel Eugenio Morocho-Cayamcela

Authors
  1. Roberto Castro
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  2. Israel Pineda
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  3. Manuel Eugenio Morocho-Cayamcela
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Corresponding author

Correspondence to Manuel Eugenio Morocho-Cayamcela .

Editor information

Editors and Affiliations

  1. Politecnica Salesiana University, Cuenca, Ecuador

    Juan Pablo Salgado Guerrero

  2. Universidad Técnica Particular de Loja, Loja, Ecuador

    Janneth Chicaiza Espinosa

  3. Politecnica Salesiana University, Cuenca, Ecuador

    Mariela Cerrada Lozada

  4. CEDIA, Cuenca, Ecuador

    Santiago Berrezueta-Guzman

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Castro, R., Pineda, I., Morocho-Cayamcela, M.E. (2021). Hyperparameter Tuning over an Attention Model for Image Captioning. In: Salgado Guerrero, J.P., Chicaiza Espinosa, J., Cerrada Lozada, M., Berrezueta-Guzman, S. (eds) Information and Communication Technologies. TICEC 2021. Communications in Computer and Information Science, vol 1456. Springer, Cham. https://doi.org/10.1007/978-3-030-89941-7_13

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  • DOI: https://doi.org/10.1007/978-3-030-89941-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89940-0

  • Online ISBN: 978-3-030-89941-7

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