閱讀小結：A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

時間 2020-12-20

標籤深度學習 CNN 物體檢測简体版

原文原文鏈接

arXiv: https://arxiv.org/pdf/1704.03414.pdf What: 1. 目標是去增強檢測器對於遮擋和形變的泛化能力 2. 但是數據集中遮擋和形變的圖像一般較少 3. 所以作者提出了 adversary的方法去增加訓練難度 4. 整個網絡是基於 Fast-RCNN 而不是 Faster-RCNN How: 1. 先pretrain Fast-RC

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1. A-Fast-RCNN:Hard Positive Generation via Adversary for Object Detection
2. CVPR17論文有感：A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
3. A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
4. [論文筆記]A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
5. [深度學習論文筆記][CVPR 17 oral]A-Fast-RCNN： Hard Positive Generation via Adversary for Object Detection
6. 對抗學習用於目標檢測--A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
7. 論文閱讀-《Focal Loss for Dense Object Detection》
8. CVPR 2018 SINT++:《SINT++: Robust Visual Tracking via Adversarial Hard Positive Generation》論文筆記
9. Focal Loss for Dense Object Detection論文閱讀
10. Feature Pyramid Networks for Object Detection 閱讀筆記
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1. A-Fast-RCNN:Hard Positive Generation via Adversary for Object Detection
2. CVPR17論文有感：A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
3. A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
4. [論文筆記]A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
5. [深度學習論文筆記][CVPR 17 oral]A-Fast-RCNN： Hard Positive Generation via Adversary for Object Detection
6. 對抗學習用於目標檢測--A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
7. 論文閱讀-《Focal Loss for Dense Object Detection》
8. CVPR 2018 SINT++:《SINT++: Robust Visual Tracking via Adversarial Hard Positive Generation》論文筆記
9. Focal Loss for Dense Object Detection論文閱讀
10. Feature Pyramid Networks for Object Detection 閱讀筆記

>>更多相關文章<<