Papers › End-to-End Attention-based Image Captioning
End-to-End Attention-based Image Captioning
Carola Sundaramoorthy, Lin Ziwen Kelvin, Mahak Sarin, Shubham Gupta
In this paper, we address the problem of image captioning specifically for molecular translation where the result would be a predicted chemical notation in InChI format for a given molecular structure. Current approaches mainly follow rule-based or CNN+RNN based methodology. However, they seem to underperform on noisy images and images with small number of distinguishable features. To overcome this, we propose an end-to-end transformer model. When compared to attention-based techniques, our proposed model outperforms on molecular datasets.
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