34 research outputs found
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
In this paper, we address the task of semantic-guided scene generation. One
open challenge in scene generation is the difficulty of the generation of small
objects and detailed local texture, which has been widely observed in global
image-level generation methods. To tackle this issue, in this work we consider
learning the scene generation in a local context, and correspondingly design a
local class-specific generative network with semantic maps as a guidance, which
separately constructs and learns sub-generators concentrating on the generation
of different classes, and is able to provide more scene details. To learn more
discriminative class-specific feature representations for the local generation,
a novel classification module is also proposed. To combine the advantage of
both the global image-level and the local class-specific generation, a joint
generation network is designed with an attention fusion module and a
dual-discriminator structure embedded. Extensive experiments on two scene image
generation tasks show superior generation performance of the proposed model.
The state-of-the-art results are established by large margins on both tasks and
on challenging public benchmarks. The source code and trained models are
available at https://github.com/Ha0Tang/LGGAN.Comment: Accepted to CVPR 2020, camera ready (10 pages) + supplementary (18
pages
Scene Graph to Image Generation with Contextualized Object Layout Refinement
Generating images from scene graphs is a challenging task that attracted
substantial interest recently. Prior works have approached this task by
generating an intermediate layout description of the target image. However, the
representation of each object in the layout was generated independently, which
resulted in high overlap, low coverage, and an overall blurry layout. We
propose a novel method that alleviates these issues by generating the entire
layout description gradually to improve inter-object dependency. We empirically
show on the COCO-STUFF dataset that our approach improves the quality of both
the intermediate layout and the final image. Our approach improves the layout
coverage by almost 20 points and drops object overlap to negligible amounts.Comment: To appear at IEEE International Conference in Image Processing (ICIP)
202