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Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

NeurIPS 2020 · Detection and segmentation › Open-set and few-shot detection · approach: Domain-adaptive segmentation · 82 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use PLCA instead of CRSTDeepLab-V2 with ResNet-101 · GTAV → CityscapesmIoU: 47.1 -> 47.7Helped
Use PLCA instead of CRSTDeepLab-V2 with ResNet-101 · SYNTHIA → Cityscapes (16 classes)mIoU: 43.8 -> 46.8Helped
Use PLCA instead of CRSTDeepLab-V2 with ResNet-101 · SYNTHIA → Cityscapes (13 classes)mIoU: 50.1 -> 54.0Helped
Add Lovász-softmax loss (ablation)Source-only segmentation model · GTAV → CityscapesmIoU: 31.5 -> 34.3Helped
Add feature association loss (ablation)Source + Lovász-softmax model · GTAV → CityscapesmIoU: 34.3 -> 39.7Helped
Add prediction association loss (ablation)Model with feature association · GTAV → CityscapesmIoU: 39.7 -> 47.4Helped
Add label-smoothing regularization (ablation)Model with feature and prediction association · GTAV → CityscapesmIoU: 47.4 -> 47.7Helped
Replace contrastive association with direct similarity encouragement (ablation)PLCA · GTAV → CityscapesmIoU: 47.7 -> 41.8Hurt
Remove spatial aggregation (ablation)PLCA · GTAV → CityscapesmIoU: 47.7 -> 45.6Hurt
Remove spatial aggregation (ablation)PLCA · SYNTHIA → Cityscapes (16 classes)mIoU: 46.8 -> 44.6Hurt

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