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Dual Prototype-Driven Objectness Decoupling for Cross-Domain Object Detection in Urban Scene

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Computer Vision – ACCV 2024 (ACCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15479))

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Abstract

Unsupervised domain adaptation aims to mitigate the domain gap between the source and the target domains. Despite domain shifts, we have observed intrinsic knowledge that spans across domains for object detection in urban driving scenes. First, it includes consistent characteristics of objects within the same category of extracted ROIs. Second, it encompasses the similarity of patterns within the extracted ROIs, relating to the positions of the foreground and background during object detection. To utilize these, we present DuPDA, a method that effectively adapts object detectors to target domains by leveraging domain-invariant knowledge to separable objectness for training. Specifically, we construct categorical and regional prototypes, each of which operates through their specialized moving alignments. These prototypes serve as valuable references for training unlabeled target objects using similarity. Leveraging these prototypes, we determine and utilize a boundary that trains separately the foreground and background regions within the target ROIs, thereby transferring the knowledge to focus on each respective region. Our DuPDA surpasses previous state-of-the-art methods in various evaluation protocols on six benchmarks.

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References

  1. Cai, Q., Pan, Y., Ngo, C.W., Tian, X., Duan, L., Yao, T.: Exploring object relation in mean teacher for cross-domain detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11457–11466 (2019)

    Google Scholar 

  2. Cao, S., Joshi, D., Gui, L.Y., Wang, Y.X.: Contrastive mean teacher for domain adaptive object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23839–23848 (2023)

    Google Scholar 

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

    Google Scholar 

  4. Chen, M., Chen, W., Yang, S., Song, J., Wang, X., Zhang, L., Yan, Y., Qi, D., Zhuang, Y., Xie, D., et al.: Learning domain adaptive object detection with probabilistic teacher. arXiv preprint arXiv:2206.06293 (2022)

  5. 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 

  6. Chen, Y., Li, W., Van Gool, L.: ROAD: Reality oriented adaptation for semantic segmentation of urban scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7892–7901 (2018)

    Google Scholar 

  7. Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: 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 

  8. Deng, J., Li, W., Chen, Y., Duan, L.: Unbiased mean teacher for cross-domain object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4091–4101 (2021)

    Google Scholar 

  9. Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., Jiao, J.: Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 994–1003 (2018)

    Google Scholar 

  10. Do, D.P., Kim, T., Na, J., Kim, J., Lee, K., Cho, K., Hwang, W.: D3t: Distinctive dual-domain teacher zigzagging across rgb-thermal gap for domain-adaptive object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23313–23322 (2024)

    Google Scholar 

  11. Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International conference on machine learning. pp. 1180–1189. PMLR (2015)

    Google Scholar 

  12. Gao, C., Liu, C., Dun, Y., Qian, X.: Csda: Learning category-scale joint feature for domain adaptive object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 11421–11430 (2023)

    Google Scholar 

  13. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE conference on computer vision and pattern recognition. pp. 3354–3361. IEEE (2012)

    Google Scholar 

  14. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp. 2961–2969 (2017)

    Google Scholar 

  15. He, M., Wang, Y., Wu, J., Wang, Y., Li, H., Li, B., Gan, W., Wu, W., Qiao, Y.: Cross domain object detection by target-perceived dual branch distillation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9570–9580 (2022)

    Google Scholar 

  16. He, Z., Zhang, L.: Multi-adversarial faster-rcnn for unrestricted object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6668–6677 (2019)

    Google Scholar 

  17. Hong, W., Wang, Z., Yang, M., Yuan, J.: Conditional generative adversarial network for structured domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1335–1344 (2018)

    Google Scholar 

  18. Hsu, C.C., Tsai, Y.H., Lin, Y.Y., Yang, M.H.: Every pixel matters: Center-aware feature alignment for domain adaptive object detector. In: European Conference on Computer Vision. pp. 733–748. Springer (2020)

    Google Scholar 

  19. Jiang, Z., Li, Y., Yang, C., Gao, P., Wang, Y., Tai, Y., Wang, C.: Prototypical contrast adaptation for domain adaptive semantic segmentation. In: European Conference on Computer Vision. pp. 36–54. Springer (2022)

    Google Scholar 

  20. 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? Proceedings of International Conference on Robotics and Automation (2017)

    Google Scholar 

  21. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012)

    Google Scholar 

  22. Li, W., Liu, X., Yuan, Y.: Sigma: Semantic-complete graph matching for domain adaptive object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5291–5300 (2022)

    Google Scholar 

  23. Li, X., Jie, Z., Wang, W., Liu, C., Yang, J., Shen, X., Lin, Z., Chen, Q., Yan, S., Feng, J.: FoveaNet: Perspective-aware urban scene parsing. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 784–792 (2017)

    Google Scholar 

  24. Li, Y.J., Dai, X., Ma, C.Y., Liu, Y.C., Chen, K., Wu, B., He, Z., Kitani, K., Vajda, P.: Cross-domain adaptive teacher for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7581–7590 (2022)

    Google Scholar 

  25. Liu, X., Li, W., Yang, Q., Li, B., Yuan, Y.: Towards robust adaptive object detection under noisy annotations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14207–14216 (2022)

    Google Scholar 

  26. Liu, Y., Wang, J., Huang, C., Wang, Y., Xu, Y.: Cigar: Cross-modality graph reasoning for domain adaptive object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23776–23786 (2023)

    Google Scholar 

  27. Munir, M.A., Khan, M.H., Sarfraz, M., Ali, M.: SSAL: Synergizing between self-training and adversarial learning for domain adaptive object detection. Adv. Neural. Inf. Process. Syst. 34, 22770–22782 (2021)

    Google Scholar 

  28. Na, J., Jung, H., Chang, H.J., Hwang, W.: Fixbi: Bridging domain spaces for unsupervised domain adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1094–1103 (2021)

    Google Scholar 

  29. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015)

    Google Scholar 

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

    Google Scholar 

  31. Shen, Z., Maheshwari, H., Yao, W., Savvides, M.: Scl: Towards accurate domain adaptive object detection via gradient detach based stacked complementary losses. arXiv preprint arXiv:1911.02559 (2019)

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

  33. VS, V., Gupta, V., Oza, P., Sindagi, V.A., Patel, V.M.: Mega-cda: Memory guided attention for category-aware unsupervised domain adaptive object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4516–4526 (2021)

    Google Scholar 

  34. Wang, W., Cao, Y., Zhang, J., He, F., Zha, Z.J., Wen, Y., Tao, D.: Exploring sequence feature alignment for domain adaptive detection transformers. In: Proceedings of the 29th ACM International Conference on Multimedia. pp. 1730–1738 (2021)

    Google Scholar 

  35. Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer gan to bridge domain gap for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 79–88 (2018)

    Google Scholar 

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

    Google Scholar 

  37. Xu, C.D., Zhao, X.R., Jin, X., Wei, X.S.: Exploring categorical regularization for domain adaptive object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11724–11733 (2020)

    Google Scholar 

  38. Xu, M., Wang, H., Ni, B., Tian, Q., Zhang, W.: Cross-domain detection via graph-induced prototype alignment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12355–12364 (2020)

    Google Scholar 

  39. Xu, Y., Sun, Y., Yang, Z., Miao, J., Yang, Y.: H2fa r-cnn: Holistic and hierarchical feature alignment for cross-domain weakly supervised object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 14329–14339 (2022)

    Google Scholar 

  40. Yoo, J., Chung, I., Kwak, N.: Unsupervised domain adaptation for one-stage object detector using offsets to bounding box. In: European Conference on Computer Vision. pp. 691–708. Springer (2022)

    Google Scholar 

  41. Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., Darrell, T.: Bdd100k: A diverse driving video database with scalable annotation tooling. arXiv preprint arXiv:1805.046872(5), 6 (2018)

  42. Yu, J., Liu, J., Wei, X., Zhou, H., Nakata, Y., Gudovskiy, D., Okuno, T., Li, J., Keutzer, K., Zhang, S.: Mttrans: Cross-domain object detection with mean teacher transformer. In: European Conference on Computer Vision. pp. 629–645. Springer (2022)

    Google Scholar 

  43. Zhang, J., Huang, J., Luo, Z., Zhang, G., Zhang, X., Lu, S.: Da-detr: Domain adaptive detection transformer with information fusion. arXiv preprint arXiv:2103.17084 (2021)

  44. Zhang, J., Huang, J., Luo, Z., Zhang, G., Zhang, X., Lu, S.: Da-detr: Domain adaptive detection transformer with information fusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23787–23798 (2023)

    Google Scholar 

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

    Google Scholar 

  46. Zhao, G., Li, G., Xu, R., Lin, L.: Collaborative training between region proposal localization and classification for domain adaptive object detection. In: European Conference on Computer Vision. pp. 86–102. Springer (2020)

    Google Scholar 

  47. Zhao, L., Wang, L.: Task-specific inconsistency alignment for domain adaptive object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14217–14226 (2022)

    Google Scholar 

  48. Zhao, Z., Wei, S., Chen, Q., Li, D., Yang, Y., Peng, Y., Liu, Y.: Masked retraining teacher-student framework for domain adaptive object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19039–19049 (2023)

    Google Scholar 

  49. Zheng, Y., Huang, D., Liu, S., Wang, Y.: Cross-domain object detection through coarse-to-fine feature adaptation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 13766–13775 (2020)

    Google Scholar 

  50. Zhou, W., Fan, H., Luo, T., Zhang, L.: Unsupervised domain adaptive detection with network stability analysis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6986–6995 (2023)

    Google Scholar 

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Acknowledgement

This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2014-3-00123, Development of High Performance Visual BigData Discovery Platform for Large-Scale Realtime Data Analysis), (IITP-2024-No.RS-2023-00255968, AI Convergence Innovation Human Resources Development), and Korea NRF grant (NRF-2022R1A2C1091402). W. Hwang is the corresponding author.

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Authors and Affiliations

  1. Ajou University, Suwon, Korea

    Taehoon Kim & Wonjun Hwang

  2. Tech Innovation Group, Medellín, Colombia

    Jaemin Na

  3. Electronics and Telecommunications Research Institute, Daejeon, South Korea

    Joong-won Hwang

  4. University of Birmingham, Birmingham, UK

    Hyung Jin Chang

Authors
  1. Taehoon Kim
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  2. Jaemin Na
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  3. Joong-won Hwang
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  4. Hyung Jin Chang
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  5. Wonjun Hwang
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Corresponding author

Correspondence to Wonjun Hwang .

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Editors and Affiliations

  1. Pohang University of Science and Technology (POSTECH), Pohang, Korea (Republic of)

    Minsu Cho

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Ivan Laptev

  3. Google, Mountain View, CA, USA

    Du Tran

  4. National University of Singapore, Singapore, Singapore

    Angela Yao

  5. Peking University, Beijing, China

    Hongbin Zha

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Kim, T., Na, J., Hwang, Jw., Chang, H.J., Hwang, W. (2025). Dual Prototype-Driven Objectness Decoupling for Cross-Domain Object Detection in Urban Scene. In: Cho, M., Laptev, I., Tran, D., Yao, A., Zha, H. (eds) Computer Vision – ACCV 2024. ACCV 2024. Lecture Notes in Computer Science, vol 15479. Springer, Singapore. https://doi.org/10.1007/978-981-96-0966-6_8

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