Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CAGAN: Text-To-Image Generation with Combined Attention GANs   Generating images according to natural language descriptions is a challenging
task. Prior research has mainly focused to enhance the quality of generation by
investigating the use of spatial attention and/or textual attention thereby
neglecting the relationship between channels. In this work, we propose the
Combined Attention Generative Adversarial Network (CAGAN) to generate
photo-realistic images according to textual descriptions. The proposed CAGAN
utilises two attention models: word attention to draw different sub-regions
conditioned on related words; and squeeze-and-excitation attention to capture
non-linear interaction among channels. With spectral normalisation to stabilise
training, our proposed CAGAN improves the state of the art on the IS and FID on
the CUB dataset and the FID on the more challenging COCO dataset. Furthermore,
we demonstrate that judging a model by a single evaluation metric can be
misleading by developing an additional model adding local self-attention which
scores a higher IS, outperforming the state of the art on the CUB dataset, but
generates unrealistic images through feature repetition.

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The categories of this paper are: math
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
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The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: eess
37
The categories of this paper are: supr-con
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The categories of this paper are: gr-qc