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DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation

CVPR 2019 · Detection and segmentation › CNN features · approach: Context modeling for semantic segmentation · 714 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace ResNet-50 with Xception A (ablation)Cityscapes val segmentation backbone · Cityscapes valmIoU: 68.3% -> 59.2%Hurt
Add ASPP to Xception A (ablation)Xception A · Cityscapes valmIoU: 59.2% -> 67.1%Helped
Add FC attention to Xception A (ablation)Xception A · Cityscapes valmIoU: 59.2 -> 65.4Helped
Add one additional backbone for sub-network aggregation (ablation)Backbone A · Cityscapes valmIoU: 65.4% -> 66.3%Helped
Add a third Backbone A encoder (ablation)Backbone A x2 · Cityscapes valmIoU: 66.3% -> 65.1%Hurt
Add a fourth Backbone A encoder (ablation)Backbone A x3 · Cityscapes valmIoU: 65.1% -> 50.8%Hurt
Add high-level feature fusion (ablation)Backbone A x3 · Cityscapes valmIoU: 65.1% -> 69.6%Helped
Add low-level feature fusion after high-level fusion (ablation)Backbone A x3 + HL · Cityscapes valmIoU: 69.6% -> 71.9%Helped
Add high-level feature fusion (ablation)Backbone B x3 · Cityscapes valmIoU: 58.2% -> 67.6%Helped
Add low-level feature fusion after high-level fusion (ablation)Backbone B x3 + HL · Cityscapes valmIoU: 67.6% -> 68.4%Helped
Reduce Backbone A input height to 512 × 1024 (ablation)DFANet A · Cityscapes testmIoU: 71.3 -> 70.3Hurt
Use the lighter Backbone B instead of Backbone A (ablation)DFANet · Cityscapes testmIoU: 71.3 -> 67.1Hurt

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