Advertisement

ForkGAN: Seeing into the Rainy Night

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12348)

Abstract

We present a ForkGAN for task-agnostic image translation that can boost multiple vision tasks in adverse weather conditions. Three tasks of image localization/retrieval, semantic image segmentation, and object detection are evaluated. The key challenge is achieving high-quality image translation without any explicit supervision, or task awareness. Our innovation is a fork-shape generator with one encoder and two decoders that disentangles the domain-specific and domain-invariant information. We force the cyclic translation between the weather conditions to go through a common encoding space, and make sure the encoding features reveal no information about the domains. Experimental results show our algorithm produces state-of-the-art image synthesis results and boost three vision tasks’ performances in adverse weathers.

Keywords

Light illumination Image-to-image translation Image synthesis Generative adversarial networks 

Notes

Acknowledgement

This work was supported by a MSRA Collaborative Research 2019 Grant.

Supplementary material

504435_1_En_10_MOESM1_ESM.zip (43.2 mb)
Supplementary material 1 (zip 44278 KB)

References

  1. 1.
    Anoosheh, A., Sattler, T., Timofte, R., Pollefeys, M., Van Gool, L.: Night-to-day image translation for retrieval-based localization. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 5958–5964. IEEE (2019)Google Scholar
  2. 2.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  3. 3.
    Chen, K., et al.: MMDetection: open MMLab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155 (2019)
  4. 4.
    Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: IEEE International Conference on Computer Vision, pp. 1511–1520 (2017)Google Scholar
  5. 5.
    Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: CVPR, pp. 8789–8797 (2018)Google Scholar
  6. 6.
    Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  7. 7.
    Halder, S.S., Lalonde, J.F., de Charette, R.: Physics-based rendering for improving robustness to rain. In: IEEE/CVF International Conference on Computer Vision (2019)Google Scholar
  8. 8.
    He, Z., Zhang, L.: Multi-adversarial Faster-RCNN for unrestricted object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6668–6677 (2019)Google Scholar
  9. 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: NIPSs, pp. 6626–6637 (2017)Google Scholar
  10. 10.
    Hu, X., Fu, C.W., Zhu, L., Heng, P.A.: Depth-attentional features for single-image rain removal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8022–8031 (2019)Google Scholar
  11. 11.
    Huang, S.-W., Lin, C.-T., Chen, S.-P., Wu, Y.-Y., Hsu, P.-H., Lai, S.-H.: AugGAN: cross domain adaptation with GAN-based data augmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 731–744. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01240-3_44CrossRefGoogle Scholar
  12. 12.
    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_11CrossRefGoogle Scholar
  13. 13.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR, pp. 5967–5976 (2017)Google Scholar
  14. 14.
    Jiang, Y., et al.: EnlightenGAN: deep light enhancement without paired supervision. arXiv preprint arXiv:1906.06972 (2019)
  15. 15.
    Kim, J., Kim, M., Kang, H., Lee, K.: U-GAT-IT: unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation. arXiv preprint arXiv:1907.10830 (2019)
  16. 16.
    Lee, H.-Y., Tseng, H.-Y., Huang, J.-B., Singh, M., Yang, M.-H.: Diverse image-to-image translation via disentangled representations. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 36–52. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01246-5_3CrossRefGoogle Scholar
  17. 17.
    Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems, pp. 700–708 (2017)Google Scholar
  18. 18.
    Lowe, D.G., et al.: Object recognition from local scale-invariant features. In: ICCV, vol. 99, pp. 1150–1157 (1999)Google Scholar
  19. 19.
    Milford, M.J., Wyeth, G.F.: SeqSLAM: visual route-based navigation for sunny summer days and stormy winter nights. In: 2012 IEEE International Conference on Robotics and Automation, pp. 1643–1649. IEEE (2012)Google Scholar
  20. 20.
    Porav, H., Bruls, T., Newman, P.: Don’t worry about the weather: unsupervised condition-dependent domain adaptation. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 33–40. IEEE (2019)Google Scholar
  21. 21.
    Porav, H., Maddern, W., Newman, P.: Adversarial training for adverse conditions: robust metric localisation using appearance transfer. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1011–1018. IEEE (2018)Google Scholar
  22. 22.
    Romera, E., Bergasa, L.M., Yang, K., Alvarez, J.M., Barea, R.: Bridging the day and night domain gap for semantic segmentation. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 1312–1318. IEEE (2019)Google Scholar
  23. 23.
    Ros, G., Alvarez, J.M.: Unsupervised image transformation for outdoor semantic labelling. In: 2015 IEEE Intelligent Vehicles Symposium (IV), pp. 537–542. IEEE (2015)Google Scholar
  24. 24.
    Sakaridis, C., Dai, D., Van Gool, L.: Semantic foggy scene understanding with synthetic data. Int. J. Comput. Vis. 126(9), 973–992 (2018)CrossRefGoogle Scholar
  25. 25.
    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
  26. 26.
    Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 472–480 (2017)Google Scholar
  27. 27.
    Yu, F., et al.: BDD100K: a diverse driving video database with scalable annotation tooling. arXiv preprint arXiv:1805.04687 (2018)
  28. 28.
    Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: International Conference on Computer Vision, pp. 2223–2232 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.UISEE Technology (Beijing) Co., Ltd.BeijingChina
  2. 2.Kyoto UniversityKyotoJapan
  3. 3.University of PennsylvaniaPhiladelphiaUSA

Personalised recommendations