  In this paper, we focus on the semantic image synthesis task that aims at
transferring semantic label maps to photo-realistic images. Existing methods
lack effective semantic constraints to preserve the semantic information and
ignore the structural correlations in both spatial and channel dimensions,
leading to unsatisfactory blurry and artifact-prone results. To address these
limitations, we propose a novel Dual Attention GAN (DAGAN) to synthesize
photo-realistic and semantically-consistent images with fine details from the
input layouts without imposing extra training overhead or modifying the network
architectures of existing methods. We also propose two novel modules, i.e.,
position-wise Spatial Attention Module (SAM) and scale-wise Channel Attention
Module (CAM), to capture semantic structure attention in spatial and channel
dimensions, respectively. Specifically, SAM selectively correlates the pixels
at each position by a spatial attention map, leading to pixels with the same
semantic label being related to each other regardless of their spatial
distances. Meanwhile, CAM selectively emphasizes the scale-wise features at
each channel by a channel attention map, which integrates associated features
among all channel maps regardless of their scales. We finally sum the outputs
of SAM and CAM to further improve feature representation. Extensive experiments
on four challenging datasets show that DAGAN achieves remarkably better results
than state-of-the-art methods, while using fewer model parameters. The source
code and trained models are available at https://github.com/Ha0Tang/DAGAN.
