Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bilinear Representation for Language-based Image Editing Using
  Conditional Generative Adversarial Networks   The task of Language-Based Image Editing (LBIE) aims at generating a target
image by editing the source image based on the given language description. The
main challenge of LBIE is to disentangle the semantics in image and text and
then combine them to generate realistic images. Therefore, the editing
performance is heavily dependent on the learned representation. In this work,
conditional generative adversarial network (cGAN) is utilized for LBIE. We find
that existing conditioning methods in cGAN lack of representation power as they
cannot learn the second-order correlation between two conditioning vectors. To
solve this problem, we propose an improved conditional layer named Bilinear
Residual Layer (BRL) to learning more powerful representations for LBIE task.
Qualitative and quantitative comparisons demonstrate that our method can
generate images with higher quality when compared to previous LBIE techniques.

10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th