Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Stylish Image Description Generation via Domain Layer Norm   Most of the existing works on image description focus on generating
expressive descriptions. The only few works that are dedicated to generating
stylish (e.g., romantic, lyric, etc.) descriptions suffer from limited style
variation and content digression. To address these limitations, we propose a
controllable stylish image description generation model. It can learn to
generate stylish image descriptions that are more related to image content and
can be trained with the arbitrary monolingual corpus without collecting new
paired image and stylish descriptions. Moreover, it enables users to generate
various stylish descriptions by plugging in style-specific parameters to
include new styles into the existing model. We achieve this capability via a
novel layer normalization layer design, which we will refer to as the Domain
Layer Norm (DLN). Extensive experimental validation and user study on various
stylish image description generation tasks are conducted to show the
competitive advantages of the proposed model.

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The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
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The categories of this paper are: econ
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
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The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph