Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Leveraging Visual Question Answering to Improve Text-to-Image Synthesis   Generating images from textual descriptions has recently attracted a lot of
interest. While current models can generate photo-realistic images of
individual objects such as birds and human faces, synthesising images with
multiple objects is still very difficult. In this paper, we propose an
effective way to combine Text-to-Image (T2I) synthesis with Visual Question
Answering (VQA) to improve the image quality and image-text alignment of
generated images by leveraging the VQA 2.0 dataset. We create additional
training samples by concatenating question and answer (QA) pairs and employ a
standard VQA model to provide the T2I model with an auxiliary learning signal.
We encourage images generated from QA pairs to look realistic and additionally
minimize an external VQA loss. Our method lowers the FID from 27.84 to 25.38
and increases the R-prec. from 83.82% to 84.79% when compared to the baseline,
which indicates that T2I synthesis can successfully be improved using a
standard VQA model.

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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th
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The categories of this paper are: nlin
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The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th
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The categories of this paper are: econ