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Research map / Detection and segmentation / CNN features

Scale-Equalizing Pyramid Convolution for Object Detection

CVPR 2020 · Detection and segmentation › CNN features · approach: Region-based and query-based detectors · 135 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace head convolutions with PConv (ablation)RetinaNet · COCO minivalAP: 35.7 -> 37.0Helped
Add integrated batch normalization after PConv (ablation)RetinaNet + PConv · COCO minivalAP: 37.0 -> 37.9Helped
Add scale-equalizing deformable kernels (ablation)RetinaNet + PConv + iBN · COCO minivalAP: 37.9 -> 39.7Helped
Use SEPC-lite rather than full SEPC (ablation)RetinaNet · COCO minivalAP: 39.7 -> 38.8Hurt
Replace head convolutions with PConv (ablation)FSAF · COCO minivalAP: 36.9 -> 38.7Helped
Add integrated batch normalization after PConv (ablation)FSAF + PConv · COCO minivalAP: 38.7 -> 38.9Helped
Add scale-equalizing deformable kernels (ablation)FSAF + PConv + iBN · COCO minivalAP: 38.9 -> 41.3Helped
Replace head convolutions with PConv (ablation)FreeAnchor · COCO minivalAP: 38.5 -> 40.0Helped
Add integrated batch normalization after PConv (ablation)FreeAnchor + PConv · COCO minivalAP: 40.0 -> 41.2Helped
Add scale-equalizing deformable kernels (ablation)FreeAnchor + PConv + iBN · COCO minivalAP: 41.2 -> 42.8Helped
Replace full SEPC with SEPC-lite (ablation)FreeAnchor · COCO minivalAP: 42.8 -> 41.7Hurt
Use shared single BN statistics across pyramid levels instead of integrated BN (ablation)Detection head · COCOAPHurt

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