Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Can We Automate Diagrammatic Reasoning?   Learning to solve diagrammatic reasoning (DR) can be a challenging but
interesting problem to the computer vision research community. It is believed
that next generation pattern recognition applications should be able to
simulate human brain to understand and analyze reasoning of images. However,
due to the lack of benchmarks of diagrammatic reasoning, the present research
primarily focuses on visual reasoning that can be applied to real-world
objects. In this paper, we present a diagrammatic reasoning dataset that
provides a large variety of DR problems. In addition, we also propose a
Knowledge-based Long Short Term Memory (KLSTM) to solve diagrammatic reasoning
problems. Our proposed analysis is arguably the first work in this research
area. Several state-of-the-art learning frameworks have been used to compare
with the proposed KLSTM framework in the present context. Preliminary results
indicate that the domain is highly related to computer vision and pattern
recognition research with several challenging avenues.

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The categories of this paper are: gr-qc
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: nlin
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The categories of this paper are: physics
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The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: q-bio
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The categories of this paper are: chem-ph