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Attentive Fashion Grammar Network for Fashion Landmark Detection and Clothing Category Classification

CVPR 2018 · Label-efficient and robust learning › Recognition and long-tail · approach: Correspondence and causal representation learning · 261 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add fashion kinematics and symmetry grammars with BCRNN message passing (ablation)VGG-16 landmark detector · DeepFashion-CNormalized error: .0615 -> .0484Helped
Add fashion kinematics and symmetry grammars with BCRNN message passing (ablation)VGG-16 landmark detector · FLDNormalized error: .0681 -> .0583Helped
Add kinematics grammar (ablation)Fashion Grammar Network landmark detector · DeepFashion-CNormalized error: .0525 -> .0484Helped
Add kinematics grammar (ablation)Fashion Grammar Network landmark detector · FLDNormalized error: .0659 -> .0583Helped
Add symmetry grammar (ablation)Fashion Grammar Network landmark detector · DeepFashion-CNormalized error: .0538 -> .0484Helped
Add symmetry grammar (ablation)Fashion Grammar Network landmark detector · FLDNormalized error: .0641 -> .0583Helped
Use three message-passing iterations rather than one (ablation)Fashion Grammar Network landmark detector · DeepFashion-CNormalized error: .0579 -> .0484Helped
Use three message-passing iterations rather than one (ablation)Fashion Grammar Network landmark detector · FLDNormalized error: .0657 -> .0583Helped
Add landmark-aware attention (ablation)Fashion Grammar Network classifier · DeepFashion-CCategory top-3 accuracy: 85.27 -> 90.99Helped
Add landmark-aware attention (ablation)Fashion Grammar Network classifier · DeepFashion-CCategory top-5 accuracy: 91.32 -> 95.78Helped
Add category-driven attention (ablation)Fashion Grammar Network classifier · DeepFashion-CCategory top-3 accuracy: 87.75 -> 90.99Helped
Add category-driven attention (ablation)Fashion Grammar Network classifier · DeepFashion-CAttribute top-3 recall: 49.93 -> 51.53Helped

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