LSTD:A Low-Shot Transfer Detector for Object Detection
zero-shot one-shot few-shot区别链接 Abstract 1,为了解决训练样本不足的问题,提出了LSTD模型,并整合了SSD与faster rcnn模型的优点(即SSD部分模型与faster rcnn部分模型结合) 2,为了进一步提高检测的微调,提出了一种新的正则化方法:基于原域和目标域的转移指数(TK)和背景抑制(BD)的正则化方法. 3,Low-s
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- 1. 论文阅读笔记《LSTD: A Low-Shot Transfer Detector for Object Detection》
- 2. 论文阅读(二)：LSTD: A Low-Shot Transfer Detector for Object Detection
- 3. Metrics for object detection
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- 5. Focal Loss for Dense Object Detection
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- 7. RetinaNet--Focal Loss for Dense Object Detection
- 8. Object Detection 之Feature Pyramid Networks for Object Detection(FPN)
- 9. ICCV2017：Focal Loss for Dense Object Detection
- 10. 论文阅读：Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
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相关文章
- 1. 论文阅读笔记《LSTD: A Low-Shot Transfer Detector for Object Detection》
- 2. 论文阅读(二)：LSTD: A Low-Shot Transfer Detector for Object Detection
- 3. Metrics for object detection
- 4. object-detection
- 5. Focal Loss for Dense Object Detection
- 6. Metrics-for-object-detection
- 7. RetinaNet--Focal Loss for Dense Object Detection
- 8. Object Detection 之Feature Pyramid Networks for Object Detection(FPN)
- 9. ICCV2017：Focal Loss for Dense Object Detection
- 10. 论文阅读：Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection