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Research map / Detection and segmentation / CNN features

Towards Learning Structure via Consensus for Face Segmentation and Parsing

CVPR 2020 · Detection and segmentation › CNN features · approach: Context modeling for semantic segmentation · 15 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace pixel-wise cross-entropy with consensus loss (ablation)COFW face segmentation · COFW testIOU_face: 84.1 -> 85.7Helped
Replace pixel-wise cross-entropy with consensus loss (ablation)COFW face segmentation · COFW testspars.: 3.8 -> 1.6Helped
Add dropout and flip augmentation (ablation)Structure via consensus on COFW · COFW testIOU_face: 85.7 -> 87.0Helped
Train with random weights rather than transfer learning (ablation)Softmax+CE face segmentation · COFW testIOU_face: 84.5 -> 76.8Hurt
Replace pixel-wise loss with consensus loss (ablation)Part Labels face parsing · Part Labels testIOU_hair: 68.95 -> 72.48Helped
Replace pixel-wise loss with consensus loss (ablation)Part Labels face parsing · Part Labels testIOU_mean: 83.65 -> 85.74Helped
Add dropout and flip augmentation (ablation)Part Labels baseline · Part Labels testIOU_hair: 68.95 -> 73.97Helped
Add dropout and flip augmentation (ablation)Structure via consensus on Part Labels · Part Labels testIOU_hair: 72.48 -> 75.84Helped
Increase input resolution from 128 to 250 (ablation)Structure via consensus on Part Labels · Part Labels testacc_p: 96.05 -> 95.86Hurt

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