  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.
