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Reflection and Rotation Symmetry Detection via Equivariant Learning

CVPR 2022 · Detection and segmentation › CNN features · approach: Context for hard detection conditions · 17 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use equivariant convolutions instead of standard convolutions (ablation)EquiSym-ref · DENDI reflectionF1 score: 55.1 to 63.1Helped
Use equivariant convolutions instead of standard convolutions (ablation)EquiSym-rot · DENDI rotationF1 score: 17.7 to 21.2Helped
Add reflection-orientation auxiliary classification (ablation)EquiSym-ref · DENDI reflectionF1 score: 63.1 to 64.5Helped
Add rotation-fold auxiliary classification (ablation)EquiSym-rot · DENDI rotationF1 score: 21.2 to 22.5Helped
Add auxiliary classification during joint training (ablation)EquiSym-joint · DENDI joint reflectionF1 score: 62.2 to 58.7Hurt
Add auxiliary classification during joint training (ablation)EquiSym-joint · DENDI joint rotationF1 score: 22.1 to 22.5Helped
Train EquiSym-ref on real images rather than real and synthetic images (ablation)EquiSym-ref · SDRW reflectionF1 score: 67.4 and 67.1No clear effect
Train EquiSym-ref on real images rather than real and synthetic images (ablation)EquiSym-ref · LDRS reflectionF1 score: 39.4 to 40.9Helped
Use EquiSym-ref instead of PMCNetReflection symmetry detection · DENDI reflectionF1 score: 52.0 to 64.5Helped
Use EquiSym-rot instead of SymResNetRotation symmetry detection · DENDI rotationF1 score: 11.9 to 22.5Helped
Use EquiSym-ref instead of PMCNetReflection symmetry detection · Mixed reflectionF1 score: 61.2 to 71.4Helped

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