Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image-to-Image Translation with Text Guidance   The goal of this paper is to embed controllable factors, i.e., natural
language descriptions, into image-to-image translation with generative
adversarial networks, which allows text descriptions to determine the visual
attributes of synthetic images. We propose four key components: (1) the
implementation of part-of-speech tagging to filter out non-semantic words in
the given description, (2) the adoption of an affine combination module to
effectively fuse different modality text and image features, (3) a novel
refined multi-stage architecture to strengthen the differential ability of
discriminators and the rectification ability of generators, and (4) a new
structure loss to further improve discriminators to better distinguish real and
synthetic images. Extensive experiments on the COCO dataset demonstrate that
our method has a superior performance on both visual realism and semantic
consistency with given descriptions.

17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat