PyTorch code for NTIRE challenge IQA method "IQMA Network: Image Quality Multi-scale Assessment Network"
I reproduce the code for the NTIRE challenge IQA method "IQMA Network: Image Quality Multi-scale Assessment Network", but I got some problems:
- In the original paper, the size of four stages feature maps extracted from the pretrained ResNet-50 is C ∗ 8 ∗ 8, C ∗ 4 ∗ 4, C ∗ 2 ∗ 2 and C ∗ 1 ∗ 1, respectively. Then, these features are concatenated to group (Fjrefi, Fjdisti, Fjrefi− Fjdisti), respectively. Finally, the concatenated features are go through two convolutional layers with size 3x3 and 2x2 for further feature extraction. Here is the question, for the 3C ∗ 4 ∗ 4, 3C ∗ 2 ∗ 2 and 3C ∗ 1 ∗ 1 features maps, how can the two convolutional layers can effectively extract the features? Espectially for the 3C ∗ 1 ∗ 1 features maps?
- According to my implement, the SRCC and PLCC results are hard to reach 0.700 on the validation set, what's the reason? Are there any fatal errors in my code?