Advances, Systems and Applications
Table 4 Comparisons with state-of-the-arts and ablation study
From: Visibility estimation via deep label distribution learning in cloud environment
Methods | Label type | FROSI | FRIDA | RMID |
|---|---|---|---|---|
VGG16 [55] | absolute | 33.0% | 26.4% | 39.8% |
ResNet [56] | absolute | 26.3% | 25.7% | 30.2% |
CNN+GRNN [7] | absolute | 23.8% | 29.1% | 35.6% |
DHCNN [57] | absolute | 28.6% | 29.2% | 41.9% |
VisNet [8] | absolute | 21.4% | 22.9% | 27.5% |
Relative CNN-RNN [16] | ranking | 14.5% | 13.8% | 18.3% |
Ours (without CNN) | distribution | 23.3% | 30.0% | 33.6% |
Ours (without RNN) | distribution | 14.6% | 16.7% | 18.1% |
Ours (without LDL) | absolute | 14.0% | 15.1% | 16.3% |
Ours (without Fusion) | distribution | 12.1% | 13.7% | 15.8% |
Ours (average fusion) | distribution | 11.3% | 11.4% | 13.9% |
Ours (voting fusion) | distribution | 11.5% | 12.0% | 13.3% |
Ours (max fusion) | distribution | 10.8% | 9.9% | 14.7% |
Ours | distribution | 9.7% | 8.9% | 11.6% |