异常检测与分割碰出火花 ECCV:Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
这篇文章提出了一个新颖的概念。AI system 必须要有 检测失败和异常的功能，比如自动驾驶和医学图像分析。 简述paper流程，基于image和segmentation map。这篇文章玩出了花火，左边是他的检测失败的功能，右边是他的异常检测的功能。 补充一下，这里异常检测的问题被定义为out-of-distribution (OOD) detection。一般的OOD detection目的
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相关文章
- 1. ECCV2020：论文解读《Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation》
- 2. 论文阅读：Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
- 3. 语义分割--Global Deconvolutional Networks for Semantic Segmentation
- 4. 语义分割--Understanding Convolution for Semantic Segmentation
- 5. RCNN：Rich feature hierarchies for accurate object detection and semantic segmentation
- 6. Rich feature hierarchies for accurate object detection and semantic segmentation
- 7. 语义分割--Fully Convolutional DenseNets for Semantic Segmentation
- 8. 语义分割，ICNet for Real-Time Semantic Segmentation
- 9. 图像分割“Understanding Convolution for Semantic Segmentation”
- 10. Squeeze-and-Attention Networks for Semantic Segmentation | CVPR2020