Hyperparameter Tuning Over an Attention Model for Image Captioning. Castro, R., Pineda, I., & Morocho-Cayamcela, M., E. In Salgado Guerrero, J., P., Chicaiza Espinosa, J., Cerrada Lozada, M., & Berrezueta-Guzman, S., editors, Information and Communication Technologies. TICEC 2021. Communications in Computer and Information Science, volume 1456, pages 172-183, 2021. Springer International Publishing. ![Hyperparameter Tuning Over an Attention Model for Image Captioning [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
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Website doi abstract bibtex 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.
@inproceedings{
title = {Hyperparameter Tuning Over an Attention Model for Image Captioning},
type = {inproceedings},
year = {2021},
pages = {172-183},
volume = {1456},
websites = {https://link.springer.com/chapter/10.1007/978-3-030-89941-7_13},
publisher = {Springer International Publishing},
city = {Guayaquil},
id = {f448a861-8787-3015-b75f-72a04fd20d88},
created = {2021-12-04T20:47:50.553Z},
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group_id = {14dc25e5-4c22-3a10-8988-092d628a7743},
last_modified = {2025-02-01T21:26:22.383Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {true},
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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.},
bibtype = {inproceedings},
author = {Castro, Roberto and Pineda, Israel and Morocho-Cayamcela, Manuel Eugenio},
editor = {Salgado Guerrero, Juan Pablo and Chicaiza Espinosa, Janneth and Cerrada Lozada, Mariela and Berrezueta-Guzman, Santiago},
doi = {https://doi.org/10.1007/978-3-030-89941-7_13},
booktitle = {Information and Communication Technologies. TICEC 2021. Communications in Computer and Information Science}
}
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