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Research map / Detection and segmentation / CNN features

A Dual Weighting Label Assignment Scheme for Object Detection

CVPR 2022 · Detection and segmentation › CNN features · approach: Box regression and loss design · 123 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use separate positive and negative weighting functionsFCOS-ResNet-50 · COCO val, 1×AP: 40.7 -> 41.5Helped
Add box refinement (ablation)DW with FCOS-ResNet-50 · COCO val, 1×AP: 41.5 -> 42.2Helped
Add a negative weighting term (ablation)DW with FCOS-ResNet-50 · COCO val, 1×AP: 39.5 -> 41.5Helped
Add P_neg to the negative weighting function (ablation)DW with FCOS-ResNet-50 · COCO val, 1×AP: 40.0 -> 41.5Helped
Add I_neg to the negative weighting function (ablation)DW with FCOS-ResNet-50 · COCO val, 1×AP: 40.5 -> 41.5Helped
Replace dual negative weighting with 1-w_pos (ablation)DW with FCOS-ResNet-50 · COCO val, 1×AP: 41.5 -> 40.7Hurt
Set positive-weight parameters β=5 and μ=5 (ablation)DW with FCOS-ResNet-50 · COCO val, 1×AP: 40.8 -> 41.5Helped
Vary negative-weight parameters γ1 and γ2 (ablation)DW with FCOS-ResNet-50 · COCO val, 1×AP: 41 to 41.5No clear effect
Change candidate bag selection method (ablation)DW with FCOS-ResNet-50 · COCO val, 1×AP: 41.1 to 41.5No clear effect
Use dual weighting instead of OTA label assignmentFCOS-ResNet-50 · COCO val, 1×AP: 40.7 -> 41.5Helped
Use dual weighting instead of OTA label assignmentFCOS-ResNet-101 · COCO test-dev 2017AP: 45.3 -> 46.2Helped
Add box refinement (ablation)DW with FCOS-ResNet-101-DCN · COCO test-dev 2017AP: 49.3 -> 49.5Helped

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