Prior-based Domain Adaptive Object Detection for Hazy and Rainy Conditions
目录 摘要 1、简介 2、相关工作 3、提出的方法 3.1、检测网络 3.2、Prior-adversarial训练 3.2、残差特征恢复模块 3.4、整体的损失 4、实验和结果 4.1、实验细节 4.2、适配到雾霾条件 4.3、适配雨场景 5、结论 摘要 恶劣的天气条件，如雾霾和雨水，会破坏捕获图像的质量，导致训练在干净图像上的检测网络在这些图像上表现不佳。为了解决这一问题，我们提出了一种无监督
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相关文章
- 1. Exploring Categorical Regularization for Domain Adaptive Object Detection
- 2. Domain Adaptive Neural Networks for Object Recognition
- 3. Domain adaptive faster-rcnn for object detection in the wild 论文笔记
- 4. 论文笔记：Domain Adaptive Faster R-CNN for Object Detection in the Wild
- 5. omain Adaptation and Adaptive Information Fusion for Object Detection on Foggy Days
- 6. （论文阅读）Strong-Weak Distribution Alignment for Adaptive Object Detection
- 7. RCNN：Rich feature hierarchies for accurate object detection and semantic segmentation
- 8. Localization and Object Detection
- 9. Metrics for object detection
- 10. 论文笔记：Strong-Weak Distribution Alignment for Adaptive Object Detection