Hyperparameter Analysis for Image Captioning

• URL: http://arxiv.org/abs/2006.10923v1
• Date: Fri, 19 Jun 2020 01:49:37 GMT
• Title: Hyperparameter Analysis for Image Captioning
• Authors: Amish Patel and Aravind Varier
• Abstract summary: We perform a thorough sensitivity analysis on state-of-the-art image captioning approaches using two different architectures: CNN+LSTM and CNN+Transformer. The biggest takeaway from the experiments is that fine-tuning the CNN encoder outperforms the baseline.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we perform a thorough sensitivity analysis on state-of-the-art image captioning approaches using two different architectures: CNN+LSTM and CNN+Transformer. Experiments were carried out using the Flickr8k dataset. The biggest takeaway from the experiments is that fine-tuning the CNN encoder outperforms the baseline and all other experiments carried out for both architectures.

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