# American Institute of Mathematical Sciences

August  2021, 4(3): 145-165. doi: 10.3934/mfc.2021009

## Global-Affine and Local-Specific Generative Adversarial Network for semantic-guided image generation

 Qufu Normal University, Qufu, China

* Corresponding author: nijch@163.com

Received  September 2020 Revised  March 2021 Published  August 2021 Early access  June 2021

The recent progress in learning image feature representations has opened the way for tasks such as label-to-image or text-to-image synthesis. However, one particular challenge widely observed in existing methods is the difficulty of synthesizing fine-grained textures and small-scale instances. In this paper, we propose a novel Global-Affine and Local-Specific Generative Adversarial Network (GALS-GAN) to explicitly construct global semantic layouts and learn distinct instance-level features. To achieve this, we adopt the graph convolutional network to calculate the instance locations and spatial relationships from scene graphs, which allows our model to obtain the high-fidelity semantic layouts. Also, a local-specific generator, where we introduce the feature filtering mechanism to separately learn semantic maps for different categories, is utilized to disentangle and generate specific visual features. Moreover, we especially apply a weight map predictor to better combine the global and local pathways considering the highly complementary between these two generation sub-networks. Extensive experiments on the COCO-Stuff and Visual Genome datasets demonstrate the superior generation performance of our model against previous methods, our approach is more capable of capturing photo-realistic local characteristics and rendering small-sized entities with more details.

Citation: Susu Zhang, Jiancheng Ni, Lijun Hou, Zili Zhou, Jie Hou, Feng Gao. Global-Affine and Local-Specific Generative Adversarial Network for semantic-guided image generation. Mathematical Foundations of Computing, 2021, 4 (3) : 145-165. doi: 10.3934/mfc.2021009
##### References:
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Lee, Inferring semantic layout for hierarchical text-to-image synthesis, IEEE Conference on Computer Vision and Pattern Recognition, (2018), 7986–7994. doi: 10.1109/CVPR.2018.00833.  Google Scholar [10] J. Johnson, A. Gupta and F. F. Li, Image generation from scene graphs, IEEE Conference on Computer Vision and Pattern Recognition, (2018), 1219–1228. doi: 10.1109/CVPR.2018.00133.  Google Scholar [11] T. Kaneko, Y. Ushiku and T. Harada, Label-noise robust generative adversarial networks, IEEE Conference on Computer Vision and Pattern Recognition, (2019), 2462–2471. doi: 10.1109/CVPR.2019.00257.  Google Scholar [12] S. W. Kim, Y. Zhou, J. Philion, A. Torralba and S. Fidler, Learning to Simulate Dynamic Environments With GameGAN, IEEE Conference on Computer Vision and Pattern Recognition, (2020), 1228–1237. doi: 10.1109/CVPR42600.2020.00131.  Google Scholar [13] D. Kingma and J. 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Jiang, Unsupervised image-to-image translation with stacked cycle-consistent adversarial networks, European Conference on Computer Vision, (2018), 186–201. doi: 10.1007/978-3-030-01240-3_12.  Google Scholar [18] W. Li, P. Zhang, L. Zhang, Q. Huang, X. He, S. Lyu and J. Gao, Object-driven text-to-image synthesis via adversarial training, IEEE Conference on Computer Vision and Pattern Recognition, (2019), 12166–12174. doi: 10.1109/CVPR.2019.01245.  Google Scholar [19] Y. Li, T. Ma, Y. Bai, N. Duan, S. Wei, and X. Wang, Pastegan: A semi-parametric method to generate image from scene graph, Advances in Neural Information Processing Systems, 2019. Google Scholar [20] B. Li, B. Zhuang, M. Li and J. Gu, Seq-SG2SL: Inferring semantic layout from scene graph through sequence to sequence learning, IEEE International Conference on Computer Vision, (2019), 7434–7442. doi: 10.1109/ICCV.2019.00753.  Google Scholar [21] S. Liu, T. Wang, D. Bau, J. Y. Zhu and A. Torralba, Diverse Image Generation via Self-Conditioned GANs, IEEE Conference on Computer Vision and Pattern Recognition, (2020), 14274–14283. doi: 10.1109/CVPR42600.2020.01429.  Google Scholar [22] S. Nam, Y. Kim and S. J. Kim, Text-adaptive generative adversarial networks: Manipulating images with natural language, Advances in Neural Information Processing Systems, (2018), 42–51. Google Scholar [23] J. C. Ni, S. S. Zhang, Z. L. Zhou, J. Hou and F. Gao, Instance Mask Embedding and Attribute-Adaptive Generative Adversarial Network for Text-to-Image Synthesis, IEEE Access, 8 (2020), 37697-37711.  doi: 10.1109/ACCESS.2020.2975841.  Google Scholar [24] T. Park, M. Y. Liu, T. C. Wang and J. Y. Zhu, Semantic image synthesis with spatially-adaptive normalization, IEEE Conference on Computer Vision and Pattern Recognition, (2019), 2332–2341. doi: 10.1109/CVPR.2019.00244.  Google Scholar [25] T. Qiao, J. Zhang, D. Xu, and D. Tao, Mirrorgan: Learning text-to-image generation by redescription, IEEE Conference on Computer Vision and Pattern Recognition, (2019), 1505–1514. Google Scholar [26] S. Ravuri and O. Vinyals, Classification accuracy score for conditional generative models, preprint, arXiv: 1905.10887. Google Scholar [27] S. Ren, K. He, R. Girshick and J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (2016), 1137-1149.  doi: 10.1109/TPAMI.2016.2577031.  Google Scholar [28] S. Sah, D. Peri, A. Shringi, C. Zhang, M. Dominguez, A. Savakis and R. Ptucha, Semantically invariant text-to-image generation, IEEE International Conference on Image Processing, (2018), 3783–3787. doi: 10.1109/ICIP.2018.8451656.  Google Scholar [29] Y. Shen, J. Gu, X. Tang and B. Zhou, Interpreting the Latent space of GANs for semantic face editing, IEEE Conference on Computer Vision and Pattern Recognition, (2020), 9240–9249. doi: 10.1109/CVPR42600.2020.00926.  Google Scholar [30] T. R. Shaham, T. Dekel and T. Michaeli, SinGAN: Learning a generative model from a single natural image, IEEE International Conference on Computer Vision, (2019), 4569–4579. doi: 10.1109/ICCV.2019.00467.  Google Scholar [31] W. Sun and T. F. Wu, Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis, preprint, arXiv: 2003.11571. Google Scholar [32] T. Sylvain, P. C. Zhang, Y. Bengio, R. D. Hjelm and S. Sharma, Object-centric image generation from layouts, preprint, arXiv: 2003.07449. Google Scholar [33] C. Szegedy, et al., Going deeper with convolutions, IEEE Conference on Computer Vision and Pattern Recognition, (2015), 1–9. doi: 10.1109/CVPR.2015.7298594.  Google Scholar [34] H. Tang, H. Liu and N. Sebe, Unified generative adversarial networks for controllable image-to-image translation, IEEE Transactions on Image Processing, 29 (2020), 8916-8929.  doi: 10.1109/TIP.2020.3021789.  Google Scholar [35] N. N. Vo and J. Hays, Localizing and orienting street views using overhead imagery, European Conference on Computer Vision, (2016), 494–509. doi: 10.1007/978-3-319-46448-0_30.  Google Scholar [36] D. M. Vo and A. Sugimoto, Visual-relation conscious image generation from structured-text, preprint, arXiv: 1908.01741. Google Scholar [37] H. Yu, Y. Huang, L. Pi and L. Wang, Recurrent deconvolutional generative adversarial networks with application to video generation, Pattern Recognition and Computer Vision, (2019), 18–28. doi: 10.1007/978-3-030-31723-2_2.  Google Scholar [38] L. Z. Zhang, J. C. Wang, Y. S. Xu, J. Min, T. Wen, J. C. Gee and J. B. Shi, Nested Scale-Editing for Conditional Image Synthesis, IEEE Conference on Computer Vision and Pattern Recognition, (2020), 5476–5486. doi: 10.1109/CVPR42600.2020.00552.  Google Scholar

show all references

##### References:
 [1] H. Caesar, J. Uijlings and V. Ferrari, COCO-Stuff: Thing and stuff classes in context, IEEE Conference on Computer Vision and Pattern Recognition, (2018), 1209–1218. doi: 10.1109/CVPR.2018.00132.  Google Scholar [2] W. L. Chen and J. Hays, Sketchygan: Towards diverse and realistic sketch to image synthesis, IEEE Conference on Computer Vision and Pattern Recognition, (2018), 9416–9425. doi: 10.1109/CVPR.2018.00981.  Google Scholar [3] B. Chen, T. Liu, K. Liu, H. Liu and S. Pei, Image Super-Resolution Using Complex Dense Block on Generative Adversarial Networks, IEEE International Conference on Image Processing, (2019), 2866–2870. doi: 10.1109/ICIP.2019.8803711.  Google Scholar [4] Y. Choi, M. Choi, M. Kim, J. M. Ha, S. H. Kim and J. Choo, Stargan: Unified generative adversarial networks for multi-domain image-to-image translation, IEEE Conference on Computer Vision and Pattern Recognition, (2018), 8789–8797. doi: 10.1109/CVPR.2018.00916.  Google Scholar [5] Y. Choi, Y. Uh, J. Yoo and J. W. Ha, StarGAN v2: Diverse image synthesis for multiple domains, IEEE Conference on Computer Vision and Pattern Recognition, (2020), 8185–8194. doi: 10.1109/CVPR42600.2020.00821.  Google Scholar [6] H. Dhamo, A. Farshad, I. Laina, N. Navab, G. D. Hager, F. Tombari and C. Rupprecht, Semantic image manipulation using scene graphs, IEEE Conference on Computer Vision and Pattern Recognition, (2020), 5212–5221. doi: 10.1109/CVPR42600.2020.00526.  Google Scholar [7] C. Gao, Q. Liu, Q. Xu, L. Wang, J. Liu and C. Zou, SketchyCOCO: Image generation from freehand scene sketches, IEEE Conference on Computer Vision and Pattern Recognition, (2020), 5173–5182. doi: 10.1109/CVPR42600.2020.00522.  Google Scholar [8] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio, Generative adversarial nets, Advances in Neural Information Processing Systems, (2014), 2672–2680. Google Scholar [9] S. Hong, D. Yang, J. Choi and H. Lee, Inferring semantic layout for hierarchical text-to-image synthesis, IEEE Conference on Computer Vision and Pattern Recognition, (2018), 7986–7994. doi: 10.1109/CVPR.2018.00833.  Google Scholar [10] J. Johnson, A. Gupta and F. F. Li, Image generation from scene graphs, IEEE Conference on Computer Vision and Pattern Recognition, (2018), 1219–1228. doi: 10.1109/CVPR.2018.00133.  Google Scholar [11] T. Kaneko, Y. Ushiku and T. Harada, Label-noise robust generative adversarial networks, IEEE Conference on Computer Vision and Pattern Recognition, (2019), 2462–2471. doi: 10.1109/CVPR.2019.00257.  Google Scholar [12] S. W. Kim, Y. Zhou, J. Philion, A. Torralba and S. Fidler, Learning to Simulate Dynamic Environments With GameGAN, IEEE Conference on Computer Vision and Pattern Recognition, (2020), 1228–1237. doi: 10.1109/CVPR42600.2020.00131.  Google Scholar [13] D. Kingma and J. Ba, Adam: A method for stochastic optimization, International Conference on Learning Representations, 2019. Google Scholar [14] T. N. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks, preprint, arXiv: 1609.02907. Google Scholar [15] R. Krishna, Visual genome: Connecting language and vision using crowdsourced dense image annotations, International Journal of Computer Vision, 123 (2017), 32-73.  doi: 10.1007/s11263-016-0981-7.  Google Scholar [16] T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar and C. L. Zitnick, Microsoft coco: Common objects in context, European Conference on Computer Vision, 8693 (2014), 740-755.  doi: 10.1007/978-3-319-10602-1_48.  Google Scholar [17] M. Li, H. Huang, L. Ma, W. Liu, T. Zhang and Y. Jiang, Unsupervised image-to-image translation with stacked cycle-consistent adversarial networks, European Conference on Computer Vision, (2018), 186–201. doi: 10.1007/978-3-030-01240-3_12.  Google Scholar [18] W. Li, P. Zhang, L. Zhang, Q. Huang, X. He, S. Lyu and J. Gao, Object-driven text-to-image synthesis via adversarial training, IEEE Conference on Computer Vision and Pattern Recognition, (2019), 12166–12174. doi: 10.1109/CVPR.2019.01245.  Google Scholar [19] Y. Li, T. Ma, Y. Bai, N. Duan, S. Wei, and X. Wang, Pastegan: A semi-parametric method to generate image from scene graph, Advances in Neural Information Processing Systems, 2019. Google Scholar [20] B. Li, B. Zhuang, M. Li and J. Gu, Seq-SG2SL: Inferring semantic layout from scene graph through sequence to sequence learning, IEEE International Conference on Computer Vision, (2019), 7434–7442. doi: 10.1109/ICCV.2019.00753.  Google Scholar [21] S. Liu, T. Wang, D. Bau, J. Y. Zhu and A. Torralba, Diverse Image Generation via Self-Conditioned GANs, IEEE Conference on Computer Vision and Pattern Recognition, (2020), 14274–14283. doi: 10.1109/CVPR42600.2020.01429.  Google Scholar [22] S. Nam, Y. Kim and S. J. Kim, Text-adaptive generative adversarial networks: Manipulating images with natural language, Advances in Neural Information Processing Systems, (2018), 42–51. Google Scholar [23] J. C. Ni, S. S. Zhang, Z. L. Zhou, J. Hou and F. Gao, Instance Mask Embedding and Attribute-Adaptive Generative Adversarial Network for Text-to-Image Synthesis, IEEE Access, 8 (2020), 37697-37711.  doi: 10.1109/ACCESS.2020.2975841.  Google Scholar [24] T. Park, M. Y. Liu, T. C. Wang and J. Y. Zhu, Semantic image synthesis with spatially-adaptive normalization, IEEE Conference on Computer Vision and Pattern Recognition, (2019), 2332–2341. doi: 10.1109/CVPR.2019.00244.  Google Scholar [25] T. Qiao, J. Zhang, D. Xu, and D. Tao, Mirrorgan: Learning text-to-image generation by redescription, IEEE Conference on Computer Vision and Pattern Recognition, (2019), 1505–1514. Google Scholar [26] S. Ravuri and O. Vinyals, Classification accuracy score for conditional generative models, preprint, arXiv: 1905.10887. Google Scholar [27] S. Ren, K. He, R. Girshick and J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (2016), 1137-1149.  doi: 10.1109/TPAMI.2016.2577031.  Google Scholar [28] S. Sah, D. Peri, A. Shringi, C. Zhang, M. Dominguez, A. Savakis and R. Ptucha, Semantically invariant text-to-image generation, IEEE International Conference on Image Processing, (2018), 3783–3787. doi: 10.1109/ICIP.2018.8451656.  Google Scholar [29] Y. Shen, J. Gu, X. Tang and B. Zhou, Interpreting the Latent space of GANs for semantic face editing, IEEE Conference on Computer Vision and Pattern Recognition, (2020), 9240–9249. doi: 10.1109/CVPR42600.2020.00926.  Google Scholar [30] T. R. Shaham, T. Dekel and T. Michaeli, SinGAN: Learning a generative model from a single natural image, IEEE International Conference on Computer Vision, (2019), 4569–4579. doi: 10.1109/ICCV.2019.00467.  Google Scholar [31] W. Sun and T. F. Wu, Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis, preprint, arXiv: 2003.11571. Google Scholar [32] T. Sylvain, P. C. Zhang, Y. Bengio, R. D. Hjelm and S. Sharma, Object-centric image generation from layouts, preprint, arXiv: 2003.07449. Google Scholar [33] C. Szegedy, et al., Going deeper with convolutions, IEEE Conference on Computer Vision and Pattern Recognition, (2015), 1–9. doi: 10.1109/CVPR.2015.7298594.  Google Scholar [34] H. Tang, H. Liu and N. Sebe, Unified generative adversarial networks for controllable image-to-image translation, IEEE Transactions on Image Processing, 29 (2020), 8916-8929.  doi: 10.1109/TIP.2020.3021789.  Google Scholar [35] N. N. Vo and J. Hays, Localizing and orienting street views using overhead imagery, European Conference on Computer Vision, (2016), 494–509. doi: 10.1007/978-3-319-46448-0_30.  Google Scholar [36] D. M. Vo and A. Sugimoto, Visual-relation conscious image generation from structured-text, preprint, arXiv: 1908.01741. Google Scholar [37] H. Yu, Y. Huang, L. Pi and L. Wang, Recurrent deconvolutional generative adversarial networks with application to video generation, Pattern Recognition and Computer Vision, (2019), 18–28. doi: 10.1007/978-3-030-31723-2_2.  Google Scholar [38] L. Z. Zhang, J. C. Wang, Y. S. Xu, J. Min, T. Wen, J. C. Gee and J. B. Shi, Nested Scale-Editing for Conditional Image Synthesis, IEEE Conference on Computer Vision and Pattern Recognition, (2020), 5476–5486. doi: 10.1109/CVPR42600.2020.00552.  Google Scholar
Overview of the proposed GALS-GAN
Illustration of a single graph convolution layer
Architecture of the MLP
Inferring process of the mask predictor
Architecture of the local-specific generator
Architecture of the multi-scale discriminators
Images generated by different level generators
Qualitative examples generated by our GALS-GAN based on the COCO-Stuff dataset
Qualitative examples generated by our GALS-GAN based on the Visual Genome dataset
Qualitative comparison of different models
An example of manipulating the synthesized image
Example results of different image manipulation types
Ablation study of the global-affine generator
Ablation study of the local-specific generator
Statistics of COCO-Stuff and Visual Genome datasets
 datasets train val test categories max min COCO-Stuff 74121 1024 2048 171 8 3 Visual Genome 62565 5506 5088 178 30 3
 datasets train val test categories max min COCO-Stuff 74121 1024 2048 171 8 3 Visual Genome 62565 5506 5088 178 30 3
Quantitative comparison of images generated by different methods on the COCO-Stuff dataset
 Methods IS $\uparrow$ FID $\downarrow$ 64 $\times$ 64 128 $\times$ 128 64 $\times$ 64 128$\times$ 128 sg2im [10] 6.7$\pm$0.1 5.99$\pm$0.27 67.99 95.18 stacking-GANs [36] 9.1$\pm$0.20 12.01$\pm$0.40 50.94 39.78 PasteGAN [19] 9.2$\pm$0.32 - 42.30 - PasteGAN (GT layout) [19] 10.20$\pm$0.20 - 34.30 - ours 9.85$\pm$0.15 13.82$\pm$0.30 38.29 29.62
 Methods IS $\uparrow$ FID $\downarrow$ 64 $\times$ 64 128 $\times$ 128 64 $\times$ 64 128$\times$ 128 sg2im [10] 6.7$\pm$0.1 5.99$\pm$0.27 67.99 95.18 stacking-GANs [36] 9.1$\pm$0.20 12.01$\pm$0.40 50.94 39.78 PasteGAN [19] 9.2$\pm$0.32 - 42.30 - PasteGAN (GT layout) [19] 10.20$\pm$0.20 - 34.30 - ours 9.85$\pm$0.15 13.82$\pm$0.30 38.29 29.62
Quantitative comparison of images generated by different methods on Visual Genome dataset
 Methods IS $\uparrow$ FID $\downarrow$ 64 $\times$ 64 128 $\times$ 128 64 $\times$ 64 128$\times$ 128 sg2im [10] 5.5$\pm$0.10 4.78$\pm$0.15 73.79 70.40 stacking-GANs [36] 6.90$\pm$0.20 9.24$\pm$0.41 59.53 50.19 PasteGAN [19] 7.97$\pm$0.30 - 58.37 - PasteGAN (GT layout) [19] 9.15$\pm$0.20 - 34.91 - ours 8.87$\pm$0.15 11.20$\pm$0.55 39.25 29.94
 Methods IS $\uparrow$ FID $\downarrow$ 64 $\times$ 64 128 $\times$ 128 64 $\times$ 64 128$\times$ 128 sg2im [10] 5.5$\pm$0.10 4.78$\pm$0.15 73.79 70.40 stacking-GANs [36] 6.90$\pm$0.20 9.24$\pm$0.41 59.53 50.19 PasteGAN [19] 7.97$\pm$0.30 - 58.37 - PasteGAN (GT layout) [19] 9.15$\pm$0.20 - 34.91 - ours 8.87$\pm$0.15 11.20$\pm$0.55 39.25 29.94
Comparison of classification accuracy
 Methods Classification Accuracy Score COCO-Stuff Visual Genome 64 $\times$ 64 128 $\times$ 128 64 $\times$ 64 128$\times$ 128 sg2im [10] 28.8 24.1 26.7 23.4 stacking-GANs [36] 33.9 31.2 32.7 30.3 PasteGAN [19] 40.3 - 38.7 - ours 46.1 44.6 45.4 43.5
 Methods Classification Accuracy Score COCO-Stuff Visual Genome 64 $\times$ 64 128 $\times$ 128 64 $\times$ 64 128$\times$ 128 sg2im [10] 28.8 24.1 26.7 23.4 stacking-GANs [36] 33.9 31.2 32.7 30.3 PasteGAN [19] 40.3 - 38.7 - ours 46.1 44.6 45.4 43.5
Quantitative comparison of predicted semantic layouts
 Methods R@0.3 R@0.5 COCO-Stuff Visual Genome COCO-Stuff Visual Genome sg2im [10] 52.4 21.9 32.2 10.6 stacking-GANs [36] 65.3 35.0 49.1 23.2 PasteGAN [19] 71.2 45.2 62.4 33.8 ours 80.7 48.4 66.2 36.5
 Methods R@0.3 R@0.5 COCO-Stuff Visual Genome COCO-Stuff Visual Genome sg2im [10] 52.4 21.9 32.2 10.6 stacking-GANs [36] 65.3 35.0 49.1 23.2 PasteGAN [19] 71.2 45.2 62.4 33.8 ours 80.7 48.4 66.2 36.5
Ablation study of GALS-GAN different architectures
 Architectures IS $\uparrow$ FID $\downarrow$ w/o $G_{g-a}$ 7.52$\pm$0.40 78.94 w/o $G_{l-s}$ 11.30$\pm$0.12 46.83 full model 13.82$\pm$0.30 29.62
 Architectures IS $\uparrow$ FID $\downarrow$ w/o $G_{g-a}$ 7.52$\pm$0.40 78.94 w/o $G_{l-s}$ 11.30$\pm$0.12 46.83 full model 13.82$\pm$0.30 29.62
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