Comments (2)
For two feature points at two views, if they are the most similar one to each other and the similarity is greater than a threshold, e.g., 0.9, this match is kept and visualized.
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@WXinlong Hi, I am also not clear with this part of paper. Would you please tell me some details:
- How to obtain feature points? Which feature is used to for matching, the feature directly from backbone(resnet output) or from the dense-head(used to compute loss)?
- Since the both the backbone and the dense-head have downsample rate of 32, how could we obtain exact position of points on the original image from the feature point?
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Related Issues (20)
- Evaluation setting on Semantic segmentation HOT 2
- Performance of Semantic Segmentation on Pascal VOC HOT 1
- Why use argmax for matching?
- dataset preparing HOT 1
- The checkpoint with neck. HOT 1
- Training an Pretrained model on object detection task on single GPU HOT 2
- DenseNeck design
- Link for pretraining Mocov2 on COCO is dead HOT 2
- Dimensions of data
- Config issue about 'Res5ROIHeadsExtraNorm'
- About Linear classification results on ImageNet
- The model and loaded state dict do not match exactly HOT 1
- How to get negative key t_
- how to train on single GPU?
- [Err]: RuntimeError: Default process group has not been initialized, please make sure to call init_process_group. HOT 2
- Neck weights
- About the loss of Denscl
- GPU training problem
- Clarification on checkpoints
- Semantic segmentation on PASCAL VOC
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