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Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence

NeurIPS 2021 · Detection and segmentation › CNN features · approach: Box regression and loss design · 234 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace Smooth L1 with KLD regression lossRetinaNet · HRSC2016AP75: 48.42 -> 72.39Helped
replace Smooth L1 with KLD regression lossR3Det · HRSC2016AP75: +33.96Helped
replace GWD with KLD lossRetinaNet · DOTA-v1.0AP50: 68.93 -> 71.28Helped
replace Smooth L1 with KLDRetinaNet · ICDAR2015 (R+F)Hmean85: 11.59 -> 11.09Hurt
replace Smooth L1 with KLDRetinaNet · MLTHmean50: +9.17Helped
replace Smooth L1/GIoU with KLD for horizontal detectionRetinaNet · MS COCOAP: 37.2 -> 38.0Helped
replace GIoU with KLDFaster RCNN · MS COCOAP: 38.3 -> 38.2No clear effect
add nonlinear transform log(D+1) to raw KLD (ablation)KLD loss · HRSC2016AP50: 0.20 -> 83.23Helped
add normalization 1-1/(tau+f(D)) with tau=1 (ablation)KLD loss · HRSC2016AP50: 83.23 -> 85.25Helped
increase tau from 1 to 5 (ablation)KLD loss · HRSC2016AP50: 85.25 -> 73.44Hurt
use symmetric/other KLD variants instead of forward KL (ablation)KLD loss · DOTA-v1.0AP50: 70.17 -> 70.71No clear effect
apply the same normalization to Smooth L1 (ablation)Smooth L1 · HRSC2016Hmean50: 84.80 -> 78.99Hurt

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