Domain adaptive faster-rcnn for object detection in the wild 论文笔记
2018年CVPR文章，基于Faster-RCNN，进行领域自适应改进（似迁移学习），用在进行雾气，光线不良情况下的野外迁移探测活动（复杂多变的场景） 解决domain shift 1.image level :scale,style,illumination 2.instance level:obj appearance ,size and orientation 主要贡献 1.概率统计论的角度
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相关文章
- 1. Domain adaptive faster-rcnn for object detection in the wild 论文笔记
- 2. 论文笔记：Domain Adaptive Faster R-CNN for Object Detection in the Wild
- 3. 论文笔记：Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels
- 4. Exploring Categorical Regularization for Domain Adaptive Object Detection
- 5. 论文笔记：Strong-Weak Distribution Alignment for Adaptive Object Detection
- 6. Traffic-Sign Detection and Classification in the Wild 论文笔记
- 7. 【论文笔记】Learning from Synthetic Data for Crowd Counting in the Wild
- 8. Material Recognition in the Wild with the Materials in Context Database论文笔记
- 9. Choosing Smartly: Adaptive Multimodal Fusion for Object Detection in Changing Environments论文阅读笔记
- 10. 论文见解之SRN: Side-output Residual Network for Object Symmetry Detection in the Wild