Comments (2)
- As shown in the figure below. On the one hand, the bounding box of the human depends on the border of it(extreme point), on the other hand, do we really need to extract the whole region feature to enhance the single point feature?
- Following the motivation, we suspect that extracting extreme point boundary point features may be important for box localization. So we compare the perfomance of the region feature, border feature and middle point of the border feature.
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thanks for the clarification!
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Related Issues (20)
- example for instance segmentation. HOT 2
- AssertionError: Requires PyTorch >= 1.3 HOT 1
- How to trun on evaluation during the training. HOT 2
- Details of enhacing the single point feature with region features? HOT 2
- 一个不成熟的小建议 HOT 2
- > 1. They are the same.
- I have the same question. HOT 1
- I wonder how you implement border align? HOT 1
- How do you realize border feature extracting in Table 1? HOT 2
- Border Feature Extraction HOT 2
- code issue HOT 1
- Can not reproduce the results. HOT 2
- where is the implementation details of border_align_forward HOT 2
- How to solve the error "cuda runtime error (98) : invalid device function" when run the borderDet? HOT 4
- Error at inference
- HTTP Error HOT 5
- no kernel image is available for execution on the device: border_align_kernel.cu:202 HOT 3
- BorderDet can not run on RTX 3090 GPU? HOT 3
- 论文图2中的实验也是在coarse的分支基础上,再添加新的分支来实现的吗
- 多尺度训练
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