论文阅读：Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
文章目录 1、论文总述 2、 feature rich and feature poor objects 3、目标检测与实例检测 4、Adding Objects to Images时的边界处理 参考文献： 1、论文总述 本篇论文是一篇对室内场景的目标检测数据集进行合成（Cut->Paste）的操作，通过这种合成操作进行数据增广，区别于一般意义上的数据增强操作，概括来讲就是：通过一个FCN网络对目
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相关文章
- 1. POLY-YOLO: HIGHER SPEED, MORE PRECISE DETECTION AND INSTANCE SEGMENTATION FOR YOLOV3论文阅读
- 2. 论文阅读-《Focal Loss for Dense Object Detection》
- 3. 论文阅读：EfficientDet: Scalable and Efficient Object Detection
- 4. iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection 论文阅读笔记
- 5. 论文阅读：Associative Embedding: End-to-End Learning for Joint Detection and Grouping
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- 7. 论文阅读：iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
- 8. [论文阅读]Image Quilting for Texture Synthesis and Transfer
- 9. [论文阅读] Focal Loss for Dense Object Detection
- 10. PANet：Path Aggregation Network for Instance Segmentation论文阅读