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MUSH: Multi-scale Hierarchical Feature Extraction for Semantic Image Synthesis

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13847))

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Abstract

Semantic image synthesis aims to translate semantic label masks to photo-realistic images. Previous methods have limitations that extract semantic features with limited convolutional kernels and ignores some crucial information, such as relative positions of pixels. To address these issues, we propose MUSH, a novel semantic image synthesis model that utilizes multi-scale information. In the generative network stage, a multi-scale hierarchical architecture is proposed for feature extraction and merged successfully with guided sampling operation to enhance semantic image synthesis. Meanwhile, in the discriminative network stage, the model contains two different modules for feature extraction of semantic masks and real images, respectively, which helps use semantic masks information more effectively. Furthermore, our proposed model achieves the state-of-the-art qualitative evaluation and quantitative metrics on some challenging datasets. Experimental results show that our method can be generalized to various models for semantic image synthesis. Our code is available at https://github.com/WangZC525/MUSH.

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References

  1. Almahairi, A., Rajeshwar, S., Sordoni, A., Bachman, P., Courville, A.: Augmented cycleGAN: learning many-to-many mappings from unpaired data. In: International Conference on Machine Learning, pp. 195–204. PMLR (2018)

    Google Scholar 

  2. Caesar, H., Uijlings, J., Ferrari, V.: Coco-stuff: thing and stuff classes in context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1209–1218 (2018)

    Google Scholar 

  3. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  4. Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1511–1520 (2017)

    Google Scholar 

  5. Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213–3223 (2016)

    Google Scholar 

  6. Dundar, A., Sapra, K., Liu, G., Tao, A., Catanzaro, B.: Panoptic-based image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8070–8079 (2020)

    Google Scholar 

  7. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)

    Google Scholar 

  8. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  9. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  10. Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1501–1510 (2017)

    Google Scholar 

  11. Huang, X., Liu, M.-Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 179–196. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_11

    Chapter  Google Scholar 

  12. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR (2015)

    Google Scholar 

  13. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)

    Google Scholar 

  14. Jiang, L., Zhang, C., Huang, M., Liu, C., Shi, J., Loy, C.C.: TSIT: a simple and versatile framework for image-to-image translation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 206–222. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_13

    Chapter  Google Scholar 

  15. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43

    Chapter  Google Scholar 

  16. Karacan, L., Akata, Z., Erdem, A., Erdem, E.: Learning to generate images of outdoor scenes from attributes and semantic layouts. arXiv preprint arXiv:1612.00215 (2016)

  17. Karacan, L., Akata, Z., Erdem, A., Erdem, E.: Manipulating attributes of natural scenes via hallucination. ACM Trans. Graph. (TOG) 39(1), 1–17 (2019)

    Article  Google Scholar 

  18. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (Poster) (2015)

    Google Scholar 

  19. Lim, J.H., Ye, J.C.: Geometric GAN. arXiv preprint arXiv:1705.02894 (2017)

  20. Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  21. Liu, X., Yin, G., Shao, J., Wang, X., et al.: Learning to predict layout-to-image conditional convolutions for semantic image synthesis. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  22. Long, J., Lu, H.: Generative adversarial networks with bi-directional normalization for semantic image synthesis. In: Proceedings of the 2021 International Conference on Multimedia Retrieval, pp. 219–226 (2021)

    Google Scholar 

  23. Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)

  24. Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: International Conference on Learning Representations (2018)

    Google Scholar 

  25. Nakashima, K.: Deeplab-pytorch. https://github.com/kazuto1011/deeplab-pytorch (2018)

  26. Ntavelis, E., Romero, A., Kastanis, I., Van Gool, L., Timofte, R.: SESAME: semantic editing of scenes by adding, manipulating or erasing objects. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12367, pp. 394–411. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58542-6_24

    Chapter  Google Scholar 

  27. Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2337–2346 (2019)

    Google Scholar 

  28. Qi, X., Chen, Q., Jia, J., Koltun, V.: Semi-parametric image synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8808–8816 (2018)

    Google Scholar 

  29. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  30. Schonfeld, E., Sushko, V., Zhang, D., Gall, J., Schiele, B., Khoreva, A.: You only need adversarial supervision for semantic image synthesis. In: International Conference on Learning Representations (2020)

    Google Scholar 

  31. Seitzer, M.: Pytorch-fid: FID score for PyTorch, August 2020. https://github.com/mseitzer/pytorch-fid. version 0.2.1

  32. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  33. Tan, Z., et al.: Diverse semantic image synthesis via probability distribution modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7962–7971 (2021)

    Google Scholar 

  34. Tan, Z., et al.: Efficient semantic image synthesis via class-adaptive normalization. IEEE Trans. Pattern Anal. Mach. Intell. 44, 4852–4866 (2021)

    Google Scholar 

  35. Tan, Z., et al.: Rethinking spatially-adaptive normalization. arXiv preprint arXiv:2004.02867 (2020)

  36. Tang, H., Bai, S., Sebe, N.: Dual attention GANs for semantic image synthesis. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1994–2002 (2020)

    Google Scholar 

  37. Tang, H., Qi, X., Xu, D., Torr, P.H., Sebe, N.: Edge guided GANs with semantic preserving for semantic image synthesis. arXiv preprint arXiv:2003.13898 (2020)

  38. Tang, H., Xu, D., Yan, Y., Torr, P.H., Sebe, N.: Local class-specific and global image-level generative adversarial networks for semantic-guided scene generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7870–7879 (2020)

    Google Scholar 

  39. Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8798–8807 (2018)

    Google Scholar 

  40. Xiao, Tete, Liu, Yingcheng, Zhou, Bolei, Jiang, Yuning, Sun, Jian: Unified perceptual parsing for scene understanding. In: Ferrari, Vittorio, Hebert, Martial, Sminchisescu, Cristian, Weiss, Yair (eds.) ECCV 2018. LNCS, vol. 11209, pp. 432–448. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01228-1_26

    Chapter  Google Scholar 

  41. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations (ICLR) (2016)

    Google Scholar 

  42. Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  43. Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. In: International Conference on Machine Learning, pp. 7354–7363. PMLR (2019)

    Google Scholar 

  44. Zhao, B., Meng, L., Yin, W., Sigal, L.: Image generation from layout. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8584–8593 (2019)

    Google Scholar 

  45. Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 633–641 (2017)

    Google Scholar 

  46. Zhou, B., et al.: Semantic understanding of scenes through the ade20k dataset. Int. J. Comput. Vision. 127, 302–321 (2018)

    Article  Google Scholar 

  47. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)

    Google Scholar 

  48. Zhu, J.Y., Zhang, R., Pathak, D., Darrell, T., Efros, A.A., Wang, O., Shechtman, E.: Toward multimodal image-to-image translation. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  49. Zhu, P., Abdal, R., Qin, Y., Wonka, P.: Sean: image synthesis with semantic region-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5104–5113 (2020)

    Google Scholar 

  50. Zhu, Z., Xu, Z., You, A., Bai, X.: Semantically multi-modal image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5467–5476 (2020)

    Google Scholar 

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Acknowledgements

This work was supported by the National Key Research and Development Program of China under Grant 2018YFB2100801.

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Authors and Affiliations

  1. Key Laboratory of Embedded System and Service Computing (Tongji University), Ministry of Education, Shanghai, 201804, China

    Zicong Wang, Qiang Ren, Junli Wang, Chungang Yan & Changjun Jiang

  2. National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, Shanghai, 201804, China

    Zicong Wang, Qiang Ren, Junli Wang, Chungang Yan & Changjun Jiang

Authors
  1. Zicong Wang
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  2. Qiang Ren
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  3. Junli Wang
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  4. Chungang Yan
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  5. Changjun Jiang
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Corresponding author

Correspondence to Changjun Jiang .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Wang, Z., Ren, Q., Wang, J., Yan, C., Jiang, C. (2023). MUSH: Multi-scale Hierarchical Feature Extraction for Semantic Image Synthesis. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13847. Springer, Cham. https://doi.org/10.1007/978-3-031-26293-7_12

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  • DOI: https://doi.org/10.1007/978-3-031-26293-7_12

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