Table 9 Quantitative evaluation of attention maps on CIFAR-10 Samples.

From: DMSCA: dynamic multi-scale channel-spatial attention for enhanced feature representation in convolutional neural networks

Method

Focus Ratio†

Semantic Consistency‡

Noise Ratio§

ResNet-18

0.65 ± 0.04

0.62 ± 0.05

0.25 ± 0.03

SE-Net

0.72 ± 0.03

0.68 ± 0.04

0.15 ± 0.02

CBAM

0.78 ± 0.03

0.74 ± 0.03

0.12 ± 0.02

ECA-Net

0.75 ± 0.04

0.71 ± 0.04

0.13 ± 0.03

CA

0.81 ± 0.02

0.77 ± 0.03

0.10 ± 0.02

DMSCA

0.87 ± 0.02

0.84 ± 0.02

0.08 ± 0.01

  1. Note: Focus ratio: Proportion of attention energy in target areas (via bounding boxes). ‡ Semantic consistency: Similarity (IoU/SSIM) to saliency maps. § Noise ratio: Energy in background (lower better). Higher is better for † and ‡. Values: mean ± SD.
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