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<p>Automatic generation of captions for a given image is an active research area in Artificial
Intelligence. The architectures have evolved from using metadata of the images on which classical
machine learning was employed to neural networks. Two different styles of architectures evolved
in the neural network space for image captioning: Encoder-Attention-Decoder architecture, and
the transformer architecture. This study is an attempt to modify the attention to allow any object
to be specified. An archetypical Encoder-Attention-Decoder architecture (Show, Attend, and Tell
(Xu et al., 2015)) is employed as a baseline for this study, and a modification of the Show, Attend,
and Tell architecture is proposed. Both the architectures are evaluated on the MSCOCO (Lin et al.,
2014) dataset, and seven metrics: BLEU – 1, 2, 3, 4 (Papineni, Roukos, Ward & Zhu, 2002),
METEOR (Banerjee & Lavie, 2005), ROGUE L (Lin, 2004), and CIDer (Vedantam, Lawrence &
Parikh, 2015) are calculated. Finally, the statistical significance of the results is evaluated by
performing paired t tests.
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| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7408883 |
| Date | 17 January 2019 |
| Creators | Hemanth Devarapalli (5930603) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Forced_Attention_for_Image_Captioning/7408883 |
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