Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Object-driven Text-to-Image Synthesis via Adversarial Training   In this paper, we propose Object-driven Attentive Generative Adversarial
Newtorks (Obj-GANs) that allow object-centered text-to-image synthesis for
complex scenes. Following the two-step (layout-image) generation process, a
novel object-driven attentive image generator is proposed to synthesize salient
objects by paying attention to the most relevant words in the text description
and the pre-generated semantic layout. In addition, a new Fast R-CNN based
object-wise discriminator is proposed to provide rich object-wise
discrimination signals on whether the synthesized object matches the text
description and the pre-generated layout. The proposed Obj-GAN significantly
outperforms the previous state of the art in various metrics on the large-scale
COCO benchmark, increasing the Inception score by 27% and decreasing the FID
score by 11%. A thorough comparison between the traditional grid attention and
the new object-driven attention is provided through analyzing their mechanisms
and visualizing their attention layers, showing insights of how the proposed
model generates complex scenes in high quality.

17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics