├── LICENSE ├── README.md └── _config.yml /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 WanyiLi 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # awsome-domain-adaptive-object-detection 2 | This repo is a collection of AWESOME things about domain adaptive object detection, including papers, code, etc. Feel free to star and fork. 3 | Most listed papers are reviewed in "Deep Domain Adaptive Object Detection: a Survey", [[2020 IEEE Symposium Series on Computational Intelligence (SSCI)]](https://ieeexplore.ieee.org/abstract/document/9308604), [[arxiv]](https://arxiv.org/abs/2002.06797). This page will be updated continuously. 4 | 5 | ## Survey 6 | 1. Deep Domain Adaptive Object Detection: a Survey. [[2020 IEEE Symposium Series on Computational Intelligence (SSCI)]](https://ieeexplore.ieee.org/abstract/document/9308604), [[arxiv]](https://arxiv.org/abs/2002.06797) 7 | 2. M. Wang and W. Deng, "Deep visual domain adaptation: A survey," Neurocomputing, vol. 312, pp. 135-153, 2018/10/27/ 2018. 8 | 3. W. M. Kouw and M. Loog, "A review of domain adaptation without target labels," IEEE transactions on pattern analysis and machine intelligence, 2019. 9 | 10 | ## Deep domain adaptive object detection (DDAOD) 11 | ### Discrepancy-based DDAOD 12 | 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]](https://arxiv.org/abs/1904.02361) [[code]](https://github.com/mkhodabandeh/robust_domain_adaptation) 13 | 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. 14 | 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. 15 | 4. Unsupervised Domain Adaptation for Multispectral Pedestrian Detection, [[CVPR 2019]](http://openaccess.thecvf.com/content_CVPRW_2019/html/MULA/Guan_Unsupervised_Domain_Adaptation_for_Multispectral_Pedestrian_Detection_CVPRW_2019_paper.html) 16 | 17 | ### Adversarial-based DDAOD 18 | 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]](https://arxiv.org/abs/1803.03243) [[CAFFE2]](https://github.com/krumo/Detectron-DA-Faster-RCNN) [[CAFFE]](https://github.com/yuhuayc/da-faster-rcnn) [[Pytorch]](https://github.com/tiancity-NJU/da-faster-rcnn-PyTorch) 19 | 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]](https://ieeexplore.ieee.org/abstract/document/8953252/) [[code]](https://github.com/xinge008/SCDA) 20 | 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]](http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Few-Shot_Adaptive_Faster_R-CNN_CVPR_2019_paper.html) [[code:A link is provided but without code yet]](https://github.com/twangnh/FAFRCNN) 21 | 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. 22 | 5. Z. He and L. Zhang, "Multi-Adversarial Faster-RCNN for Unrestricted Object Detection," presented at the International Conference on Computer Vision, 2019. 23 | 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. 24 | 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. 25 | 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. 26 | 9. Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation. [[arxiv,3 Feb 2020]](https://arxiv.org/abs/2002.00575v1) 27 | 28 | 29 | ### Reconstruction-based DDAOD 30 | 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]](https://ieeexplore.ieee.org/document/8852008),[[Project]](https://github.com/LCAD-UFES/publications-arruda-ijcnn-2019) 31 | 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. 32 | 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. 33 | 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. 34 | 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. 35 | 36 | ### Hybrid DDAOD 37 | 38 | 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. 39 | 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. 40 | 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. 41 | 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. 42 | 5. A. L. Rodriguez and K. Mikolajczyk, "Domain Adaptation for Object Detection via Style Consistency," arXiv preprint arXiv:1911.10033, 2019. 43 | 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]](https://arxiv.org/abs/1910.11319) 44 | 45 | 46 | 47 | ## Traditional domain adaptive object detection 48 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-time-machine --------------------------------------------------------------------------------