Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Reference79 articles.
1. Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010). A theory of learning from different domains. Machine Learning, 79(1), 694–711.
2. Cai, Q., Pan, Y., Ngo, C.W., Tian, X., Duan, L., & Yao, T. (2019). Exploring object relation in mean teacher for cross-domain detection. In Conference on computer vision and pattern recognition (CVPR).
3. Chen, Y., Dai, D., Pont-Tuset, J., & Van Gool, L. (2016). Scale-aware alignment of hierarchical image segmentation. In Computer vision and pattern recognition (CVPR).
4. Chen, Y., Li, W., Chen, X., & Gool, L. V. (2019). Learning semantic segmentation from synthetic data: A geometrically guided input-output adaptation approach. In Conference on computer vision and pattern recognition (CVPR).
5. Chen, Y., Li, W., Sakaridis, C., Dai, D., & Van Gool, L. (2018). Domain adaptive faster r-cnn for object detection in the wild. In Conference on computer vision and pattern recognition (CVPR).
Cited by
57 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献