Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-End Attention-based Image Captioning   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.

10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
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The categories of this paper are: eess
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: q-fin
21
The categories of this paper are: adap-org