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Thanks for your time. I want to know how you set the radius=5. The get_neighbor_images_patch_color_similarity(image_t, image_t+1, kernel=3, di=3) function seems to compute patch similarity of one pixel on image_t+1 to the corresponding image_t patch with radius of 3 (9 pixels). Then, during matching, the topk_mask function computes the top 5 of these 9 pixels. But according to the Implementation details, it seems like computing top 5 of 25 pixels. (radius=5, K=5).
Did you achieve a larger radius by using dilated convolution? Or the radius in your paper only means the receptive field of one patch, and only samples 9 pixels around the region for choosing topk?
Looking forward to your reply.
The text was updated successfully, but these errors were encountered:
Thanks for your time. I want to know how you set the radius=5. The get_neighbor_images_patch_color_similarity(image_t, image_t+1, kernel=3, di=3) function seems to compute patch similarity of one pixel on image_t+1 to the corresponding image_t patch with radius of 3 (9 pixels). Then, during matching, the topk_mask function computes the top 5 of these 9 pixels. But according to the Implementation details, it seems like computing top 5 of 25 pixels. (radius=5, K=5).
Did you achieve a larger radius by using dilated convolution? Or the radius in your paper only means the receptive field of one patch, and only samples 9 pixels around the region for choosing topk?
Looking forward to your reply.
The text was updated successfully, but these errors were encountered: