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Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
Hao Tang, Dan Xu, Yan Yan, Philip H. S. Torr, Nicu Sebe
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cross-View Image-to-Image Translation | Dayton (256×256) - aerial-to-ground | LGGAN | KL | 2.18 | #2 of 6 | Archive leaderboard | report |
| Cross-View Image-to-Image Translation | Dayton (256×256) - aerial-to-ground | LGGAN | PSNR | 22.9949 | #2 of 6 | Archive leaderboard | report |
| Cross-View Image-to-Image Translation | Dayton (256×256) - aerial-to-ground | LGGAN | SD | 19.6145 | #2 of 6 | Archive leaderboard | report |
| Cross-View Image-to-Image Translation | Dayton (256×256) - aerial-to-ground | LGGAN | SSIM | 0.5457 | #2 of 6 | Archive leaderboard | report |
| Cross-View Image-to-Image Translation | cvusa | LGGAN | KL | 2.55 | #3 of 7 | Archive leaderboard | report |
| Cross-View Image-to-Image Translation | cvusa | LGGAN | PSNR | 22.5766 | #3 of 7 | Archive leaderboard | report |
| Cross-View Image-to-Image Translation | cvusa | LGGAN | SD | 19.744 | #3 of 7 | Archive leaderboard | report |
| Cross-View Image-to-Image Translation | cvusa | LGGAN | SSIM | 0.5238 | #3 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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