Local and Global GANs With Semantic-Aware Upsampling for Image Generation

In this paper, we address the task of semantic-guided image generation. One challenge common to most existing image-level generation methods is the difficulty in generating small objects and detailed local textures. To address this, in this work we consider generating images using local context. As...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2023-01, Vol.45 (1), p.768-784
Main Authors: Tang, Hao, Shao, Ling, Torr, Philip H.S., Sebe, Nicu
Format: Article
Language:English
Subjects:
feature upsampling
GANs
Generative adversarial networks
Generators
Image classification
image generation
Image processing
Image synthesis
Interpolation
Kernel
local and global
Modules
semantic-guided
Semantics
Source code
Task analysis
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Summary:In this paper, we address the task of semantic-guided image generation. One challenge common to most existing image-level generation methods is the difficulty in generating small objects and detailed local textures. To address this, in this work we consider generating images using local context. As such, we design a local class-specific generative network using semantic maps as guidance, which separately constructs and learns subgenerators for different classes, enabling it to capture finer details. To learn more discriminative class-specific feature representations for the local generation, we also propose a novel classification module. To combine the advantages of both global image-level and local class-specific generation, a joint generation network is designed with an attention fusion module and a dual-discriminator structure embedded. Lastly, we propose a novel semantic-aware upsampling method, which has a larger receptive field and can take far-away pixels that are semantically related for feature upsampling, enabling it to better preserve semantic consistency for instances with the same semantic labels. Extensive experiments on two image generation tasks show the superior performance of the proposed method. State-of-the-art results are established by large margins on both tasks and on nine challenging public benchmarks. The source code and trained models are available at https://github.com/Ha0Tang/LGGAN .
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2022.3155989