shuida
commented
5 years ago It can be proved that every point in the embedding corresponds to an 87*87 area in the search image. A 66 area in the embedding corresponds to a 127*127 area, the same size as the exemplar image. Two adjacent points in the embedding correspond to two 87*87 areas whose centers are at a distance of 8. The stride 8=2*2*2 originated from conv1, pool1 and pool2, whose stride are 2. A point in the response is obtained by a 6*6 area in the exemplar embedding correlating with a same size area in the search embedding, which means the similarity between exemplar image and an 127\127 area in the search image.
dengyang1
您好： 我想问个问题，就是在Inference阶段，在得到距离中心点的差值后，为啥要乘以网络步长： disp_instance_input = disp_instance_feat * self.model_config['embed_config']['stride'] 可能我不太明白这个算法
谢谢所做的工作。