Skip to main content
SpringerLink
Log in

FIT: Frequency-Based Image Translation for Domain Adaptive Object Detection

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13625))

Included in the following conference series:

Abstract

Domain adaptive object detection (DAOD) aims to adapt the detector from a labelled source domain to an unlabelled target domain. In recent years, DAOD has attracted massive attention since it can alleviate performance degradation due to the large shift of data distributions in the wild. To align distributions between domains, adversarial learning is widely used in existing DAOD methods. However, the decision boundary for the adversarial domain discriminator may be inaccurate, causing the model biased towards the source domain. To alleviate this bias, we propose a novel Frequency-based Image Translation (FIT) framework for DAOD. First, by keeping domain-invariant frequency components and swapping domain-specific ones, we conduct image translation to reduce domain shift at the input level. Second, hierarchical adversarial feature learning is utilized to further mitigate the domain gap at the feature level. Finally, we design a joint loss to train the entire network in an end-to-end manner without extra training to obtain translated images. Extensive experiments on three challenging DAOD benchmarks demonstrate the effectiveness of our method.

This work was supported in part by the National Key Research and Development Plan of China under Grant 2020AAA0108902 and the Strategic Priority Research Program of Chinese Academy of Science under Grant XDB32050100.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)

    Google Scholar 

  2. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  3. Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9627–9636 (2019)

    Google Scholar 

  4. Saito, K., Ushiku, Y., Harada, T., Saenko, K.: Strong-weak distribution alignment for adaptive object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6956–6965 (2019)

    Google Scholar 

  5. Chen, C., Zheng, Z., Ding, X., Huang, Y., Dou, Q.: Harmonizing transferability and discriminability for adapting object detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8869–8878 (2020)

    Google Scholar 

  6. Chen, Y., Li, W., Sakaridis, C., Dai, D., Van Gool, L.: Domain adaptive faster R-CNN for object detection in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3339–3348 (2018)

    Google Scholar 

  7. Su, P., et al.: Adapting object detectors with conditional domain normalization. In: Vedaldi, Andrea, Bischof, Horst, Brox, Thomas, Frahm, Jan-Michael. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 403–419. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_24

    Chapter  Google Scholar 

  8. Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180–1189. PMLR (2015)

    Google Scholar 

  9. Kim, T., Jeong, M., Kim, S., Choi, S., Kim, C.: Diversify and match: a domain adaptive representation learning paradigm for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12456–12465 (2019)

    Google Scholar 

  10. 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 

  11. 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 

  12. Sakaridis, C., Dai, D., Van Gool, L.: Semantic foggy scene understanding with synthetic data. Int. J. Comput. Vis. 126(9), 973–992 (2018). https://doi.org/10.1007/s11263-018-1072-8

    Article  Google Scholar 

  13. Johnson-Roberson, M., Barto, C., Mehta, R., Sridhar, S.N., Rosaen, K., Vasudevan, R.: Driving in the matrix: can virtual worlds replace human-generated annotations for real world tasks? In: 2017 IEEE International Conference on Robotics and Automation, pp. 746–753. IEEE (2017)

    Google Scholar 

  14. Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)

    Article  Google Scholar 

  15. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  16. Hsu, H.K., et al.: Progressive domain adaptation for object detection. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 749–757 (2020)

    Google Scholar 

  17. Oppenheim, A.V.: Discrete-Time Signal Processing. Pearson Education India (1999)

    Google Scholar 

  18. Jang, E., Gu, S., Poole, B.: Categorical reparameterization with Gumbel-Softmax. arXiv preprint arXiv:1611.01144 (2016)

  19. Li, J., Duan, L.Y., Chen, X., Huang, T., Tian, Y.: Finding the secret of image saliency in the frequency domain. IEEE Trans. Pattern Anal. Mach. Intell. 37(12), 2428–2440 (2015)

    Article  Google Scholar 

  20. He, Zhenwei, Zhang, Lei: Domain adaptive object detection via asymmetric tri-way faster-RCNN. In: Vedaldi, Andrea, Bischof, Horst, Brox, Thomas, Frahm, Jan-Michael. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 309–324. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58586-0_19

    Chapter  Google Scholar 

  21. Shen, Z., et al.: CDTD: a large-scale cross-domain benchmark for instance-level image-to-image translation and domain adaptive object detection. Int. J. Comput. Vis. 129(3), 761–780 (2021). https://doi.org/10.1007/s11263-020-01394-z

    Article  MathSciNet  Google Scholar 

  22. Wu, A., Liu, R., Han, Y., Zhu, L., Yang, Y.: Vector-decomposed disentanglement for domain-invariant object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9342–9351 (2021)

    Google Scholar 

  23. Zhang, Y., Wang, Z., Mao, Y.: RPN prototype alignment for domain adaptive object detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12425–12434 (2021)

    Google Scholar 

  24. Liu, D., et al.: Decompose to adapt: cross-domain object detection via feature disentanglement. IEEE Trans. Multimed. (2022)

    Google Scholar 

  25. Chen, Y., Li, G., Jin, C., Liu, S., Li, T.: SSD-GAN: measuring the realness in the spatial and spectral domains. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 1105–1112 (2021)

    Google Scholar 

  26. Piotrowski, L.N., Campbell, F.W.: A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase. Perception 11(3), 337–346 (1982)

    Article  Google Scholar 

  27. Sankaranarayanan, S., Balaji, Y., Jain, A., Lim, S.N., Chellappa, R.: Learning from synthetic data: addressing domain shift for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3752–3761 (2018)

    Google Scholar 

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

  29. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Key Research and Development Plan of China under Grant 2020AAA0108902 and the Strategic Priority Research Program of Chinese Academy of Science under Grant XDB32050100.

Author information

Authors and Affiliations

  1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Siqi Zhang, Lu Zhang, Zhiyong Liu & Hangtao Feng

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China

    Siqi Zhang, Zhiyong Liu & Hangtao Feng

Authors
  1. Siqi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiyong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hangtao Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhiyong Liu .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, S., Zhang, L., Liu, Z., Feng, H. (2023). FIT: Frequency-Based Image Translation for Domain Adaptive Object Detection. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-30111-7_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30110-0

  • Online ISBN: 978-3-031-30111-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation