論文閱讀：Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
文章目錄 1、論文總述 2、 feature rich and feature poor objects 3、目標檢測與實例檢測 4、Adding Objects to Images時的邊界處理 參考文獻： 1、論文總述 本篇論文是一篇對室內場景的目標檢測數據集進行合成（Cut->Paste）的操作，通過這種合成操作進行數據增廣，區別於一般意義上的數據增強操作，概括來講就是：通過一個FCN網絡對目
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相關文章
- 1. POLY-YOLO: HIGHER SPEED, MORE PRECISE DETECTION AND INSTANCE SEGMENTATION FOR YOLOV3論文閱讀
- 2. 論文閱讀-《Focal Loss for Dense Object Detection》
- 3. 論文閱讀：EfficientDet: Scalable and Efficient Object Detection
- 4. iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection 論文閱讀筆記
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- 6. 論文閱讀-《Relation Networks for Object Detection》
- 7. 論文閱讀：iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
- 8. [論文閱讀]Image Quilting for Texture Synthesis and Transfer
- 9. [論文閱讀] Focal Loss for Dense Object Detection
- 10. PANet：Path Aggregation Network for Instance Segmentation論文閱讀