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Dynamic Coarse-To-Fine Learning for Oriented Tiny Object Detection

CVPR 2023 · Detection and segmentation › CNN features · approach: Box regression and loss design · 173 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use DCFL instead of S²A-NetOne-stage oriented detector with ResNet-50 · DOTA-v2.0 OBB testmAP: 49.86 -> 51.57Helped
Use DCFL instead of GGHLOne-stage oriented detector · DOTA-v1.0 OBB testmAP: 73.98 -> 74.26Helped
Use DCFL instead of ReDetOriented detector · DOTA-v1.5 OBB testmAP: 66.86 -> 67.37Helped
Use DCFL instead of Oriented RepOriented detector · DIOR-RmAP: 66.71 -> 66.80Helped
Add CPS and posterior-ranked MPS to the baseline (ablation)RetinaNet-OBB · DOTA-v2.0 valmAP: 51.70 -> 53.41Helped
Add DGMM with CPS (ablation)RetinaNet-OBB · DOTA-v2.0 valmAP: 51.70 -> 57.20Helped
Use CPS, MPS, and DGMM together (ablation)RetinaNet-OBB · DOTA-v2.0 valmAP: 51.70 -> 59.15Helped
Replace cross-layer GJSD CPS with all-layer Gaussian matching (ablation)DCFL · DOTA-v2.0 valmAP: 59.15 -> 50.12Hurt
Replace cross-layer GJSD CPS with single-layer Gaussian matching (ablation)DCFL · DOTA-v2.0 valmAP: 59.15 -> 56.72Hurt
Replace GJSD with KLD for cross-layer CPS (ablation)DCFL · DOTA-v2.0 valmAP: 59.15 -> 57.82Hurt
Replace GJSD with GWD for cross-layer CPS (ablation)DCFL · DOTA-v2.0 valmAP: 59.15 -> 58.55Hurt
Add DCN to the PCB (ablation)DCFL · DOTA-v2.0 valmAP: 58.07 -> 58.41Helped

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