【Bias 03】Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

时间 2021-06-10

标签 Bias 繁體版

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Related work Improving corruption robustness 去除腐蚀：（1）提出一种基于DNN，恢复雨雾图片质量的方法。（2）预处理中去除雨。但这种方法都是针对某种腐蚀。数据增强：把腐蚀数据加入训练。（1）blurred images上分类器表现脆弱，通过在blurred images上fine-tune提高对它的鲁棒性。（2）在一种腐蚀上fine-tune，并不能

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1. 【数据增强】Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
2. Pillar-based Object Detection for Autonomous Driving
3. 解读ECCV2020：Pillar-based Object Detection for Autonomous Driving
4. Multi-View 3D Object Detection Network for Autonomous Driving
5. Stereo R-CNN based 3D Object Detection for Autonomous Driving
6. Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving Yurong
7. 每天一篇论文 373/1000 PSEUDO-LIDAR++:ACCURATE DEPTH FOR 3D OBJECT DETECTION IN AUTONOMOUS DRIVING
8. 论文阅读CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
9. [论文解读]Multi-View 3D Object Detection Network for Autonomous Driving
10. MV3D：Multi-View 3D Object Detection Network for Autonomous Driving（翻译）
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1. 【数据增强】Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
2. Pillar-based Object Detection for Autonomous Driving
3. 解读ECCV2020：Pillar-based Object Detection for Autonomous Driving
4. Multi-View 3D Object Detection Network for Autonomous Driving
5. Stereo R-CNN based 3D Object Detection for Autonomous Driving
6. Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving Yurong
7. 每天一篇论文 373/1000 PSEUDO-LIDAR++:ACCURATE DEPTH FOR 3D OBJECT DETECTION IN AUTONOMOUS DRIVING
8. 论文阅读CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
9. [论文解读]Multi-View 3D Object Detection Network for Autonomous Driving
10. MV3D：Multi-View 3D Object Detection Network for Autonomous Driving（翻译）

>>更多相关文章<<