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CASENet: Deep Category-Aware Semantic Edge Detection

CVPR 2017 · Detection and segmentation › CNN features · approach: Context modeling for semantic segmentation · 316 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use CASENet instead of DSNResNet-101 semantic edge detection · SBD testMF: 70.3 -> 71.4Helped
use CASENet instead of DSNResNet-101 semantic edge detection · Cityscapes validationMF: 68.5 -> 71.3Helped
use CASENet instead of DSNResNet-101 semantic edge detection · Cityscapes validationAP: 66.4 -> 70.8Helped
add Side-5 supervision (ablation)CASENet · SBD testMF: 70.7 -> 71.4Helped
replace multi-label loss with reweighted softmax loss (ablation)Basic architecture with ResNet · SBD testMF: 70.6 -> 60.2Hurt
replace multi-label loss with reweighted softmax loss (ablation)Basic architecture with VGG · SBD testMF: 55.1 -> 51.1Hurt
use CASENet architecture instead of Basic (ablation)ResNet-101 semantic edge detection · SBD testMF: 70.6 -> 71.4Helped
use CASENet architecture instead of Basic (ablation)VGG semantic edge detection · SBD testMF: 55.1 -> 56.8Helped
supervise lower sides with class-specific edge labels (ablation)CASENet side features · SBD testMFHurt
keep stride 2 in res1 (ablation)CASENet · SBD at lower input resolutionMFHurt
add binary edge losses to Side-1–3 (ablation)CASENet-edge · SBDMFHurt

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