awsome-domain-adaptive-object-detection

This repo is a collection of AWESOME things about domain adaptive object detection, including papers, code, etc. Feel free to star and fork. Most listed papers are reviewed in "Deep Domain Adaptive Object Detection: a Survey", [2020 IEEE Symposium Series on Computational Intelligence (SSCI)], [arxiv]. This page will be updated continuously.

Survey

1. Deep Domain Adaptive Object Detection: a Survey. [2020 IEEE Symposium Series on Computational Intelligence (SSCI)], [arxiv]
2. M. Wang and W. Deng, "Deep visual domain adaptation: A survey," Neurocomputing, vol. 312, pp. 135-153, 2018/10/27/ 2018.
3. W. M. Kouw and M. Loog, "A review of domain adaptation without target labels," IEEE transactions on pattern analysis and machine intelligence, 2019.

Deep domain adaptive object detection (DDAOD)

Discrepancy-based DDAOD

1. M. Khodabandeh, A. Vahdat, M. Ranjbar, and W. G. Macready, "A Robust Learning Approach to Domain Adaptive Object Detection," arXiv preprint arXiv:1904.02361, 2019. [ICCV 2019] [code]
2. Q. Cai, Y. Pan, C.-W. Ngo, X. Tian, L. Duan, and T. Yao, "Exploring Object Relation in Mean Teacher for Cross-Domain Detection," presented at the Computer Vision and Pattern Recognition, 2019.
3. Y. Cao, D. Guan, W. Huang, J. Yang, Y. Cao, and Y. Qiao, "Pedestrian detection with unsupervised multispectral feature learning using deep neural networks," information fusion, vol. 46, pp. 206-217, 3/1/2019 2019.
4. Unsupervised Domain Adaptation for Multispectral Pedestrian Detection, [CVPR 2019]

Adversarial-based DDAOD

1. Y. Chen, W. Li, C. Sakaridis, D. Dai, and L. Van Gool, "Domain Adaptive Faster R-CNN for Object Detection in the Wild," computer vision and pattern recognition, pp. 3339-3348, 2018. [CVPR 2018] [CAFFE2] [CAFFE] [Pytorch]
2. X. Zhu, J. Pang, C. Yang, J. Shi, and D. Lin, "Adapting Object Detectors via Selective Cross-Domain Alignment," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 687-696. [CVPR 2019] [code]
3. T. Wang, X. Zhang, L. Yuan, and J. Feng, "Few-shot Adaptive Faster R-CNN," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 7173-7182. [CVPR 2019] [code:A link is provided but without code yet]
4. K. Saito, Y. Ushiku, T. Harada, and K. Saenko, "Strong-Weak Distribution Alignment for Adaptive Object Detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 6956-6965.
5. Z. He and L. Zhang, "Multi-Adversarial Faster-RCNN for Unrestricted Object Detection," presented at the International Conference on Computer Vision, 2019.
6. Z. Shen, H. Maheshwari, W. Yao, and M. Savvides, "SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses," ed, 2019.
7. H. Zhang, Y. Tian, K. Wang, H. He, and F.-Y. Wang, "Synthetic-to-Real Domain Adaptation for Object Instance Segmentation," in 2019 International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1-7.
8. C. Zhuang, X. Han, W. Huang, and M. R. Scott, "iFAN: Image-Instance Full Alignment Networks for Adaptive Object Detection," in AAAI Conference on Artificial Intelligence (AAAI), 2020.
9. Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation. [arxiv,3 Feb 2020]

Reconstruction-based DDAOD

1. V. F. Arruda, T. M. Paixão, R. F. Berriel, A. F. D. Souza, C. Badue, N. Sebe, et al., "Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night," in 2019 International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1-8. [IJCNN 2019 Oral],[Project]
2. C. Lin, "Cross Domain Adaptation for on-Road Object Detection Using Multimodal Structure-Consistent Image-to-Image Translation," in 2019 IEEE International Conference on Image Processing (ICIP), 2019, pp. 3029-3030.
3. T. Guo, C. P. Huynh, and M. Solh, "Domain-Adaptive Pedestrian Detection in Thermal Images," in 2019 IEEE International Conference on Image Processing (ICIP), 2019, pp. 1660-1664.
4. C. Devaguptapu, N. Akolekar, M. M. Sharma, and V. N. Balasubramanian, "Borrow From Anywhere: Pseudo Multi-Modal Object Detection in Thermal Imagery," presented at the Computer Vision and Pattern Recognition, 2019.
5. S. Liu, V. John, E. Blasch, Z. Liu, and Y. Huang, "IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018, pp. 1234-12347.

Hybrid DDAOD

1. N. Inoue, R. Furuta, T. Yamasaki, and K. Aizawa, "Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation," computer vision and pattern recognition, pp. 5001-5009, 2018.
2. Y. Shan, W. F. Lu, and C. M. Chew, "Pixel and feature level based domain adaptation for object detection in autonomous driving," Neurocomputing, vol. 367, pp. 31-38, 2019.
3. T. Kim, M. Jeong, S. Kim, S. Choi, and C. Kim, "Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 12456-12465.
4. S. Kim, J. Choi, T. Kim, and C. Kim, "Self-training and adversarial background regularization for unsupervised domain adaptive one-stage object detection," in Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 6092-6101.
5. A. L. Rodriguez and K. Mikolajczyk, "Domain Adaptation for Object Detection via Style Consistency," arXiv preprint arXiv:1911.10033, 2019.
6. H.-K. Hsu, C.-H. Yao, Y.-H. Tsai, W.-C. Hung, H.-Y. Tseng, M. Singh, et al., "Progressive Domain Adaptation for Object Detection," in Winter Conference on Applications of Computer Vision (WACV), 2020. [WACV 2020]

Traditional domain adaptive object detection