Hi, I noticed that there's a fast_acvnet_plus in the model folder, which is implemented differently compare to the paper (the correlation is computed between feature maps of 1/4 of the original resolution instead of 1/8), So if I want to reproduce the result of fast_acvnet, I think I should instead use the model in Fast_ACV.py ? Also, what benefit of fast_acvnet_plus compare to the vanilla fast_acvnet? Thanks in davance
Thank you for your attention. The fast_acvnet_plus constructs a higher resolution correlation (i.e. 1/4), which can obtain more accurate attention weights.
Hi, I noticed that there's a fast_acvnet_plus in the model folder, which is implemented differently compare to the paper (the correlation is computed between feature maps of 1/4 of the original resolution instead of 1/8), So if I want to reproduce the result of fast_acvnet, I think I should instead use the model in Fast_ACV.py ? Also, what benefit of fast_acvnet_plus compare to the vanilla fast_acvnet? Thanks in davance