異常檢測與分割碰出火花 ECCV:Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
這篇文章提出了一個新穎的概念。AI system 必須要有 檢測失敗和異常的功能，比如自動駕駛和醫學圖像分析。 簡述paper流程，基於image和segmentation map。這篇文章玩出了花火，左邊是他的檢測失敗的功能，右邊是他的異常檢測的功能。 補充一下，這裏異常檢測的問題被定義爲out-of-distribution (OOD) detection。一般的OOD detection目的
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相關文章
- 1. ECCV2020：論文解讀《Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation》
- 2. 論文閱讀：Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
- 3. 語義分割--Global Deconvolutional Networks for Semantic Segmentation
- 4. 語義分割--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. 語義分割--Fully Convolutional DenseNets for Semantic Segmentation
- 8. 語義分割，ICNet for Real-Time Semantic Segmentation
- 9. 圖像分割「Understanding Convolution for Semantic Segmentation」
- 10. Squeeze-and-Attention Networks for Semantic Segmentation | CVPR2020