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Weakly Supervised Rotation-Invariant Aerial Object Detection Network

CVPR 2022 · Detection and segmentation › Weakly supervised detection and segmentation · approach: Weakly supervised detection · 56 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use RINet's rotation-consistent supervision and instance mining instead of TCANetWeakly supervised aerial object detection · NWPU VHR-10.v2 testingmAP: 58.8 -> 70.4Helped
Use RINet's rotation-consistent supervision and instance mining instead of TCANetWeakly supervised aerial object detection · DIOR testingmAP: 25.8 -> 28.3Helped
Use RINet's rotation-consistent supervision and instance mining instead of TCANetWeakly supervised aerial object detection · DIOR trainvalCorLoc: 49.4 -> 52.8Helped
Add rotation-invariant learning to the OICR baseline (ablation)RINet · DIOR trainval and testingmAP: 18.7 -> 26.6Helped
Add rotation-invariant learning to the OICR baseline (ablation)RINet · DIOR trainval and testingCorLoc: 43.3 -> 48.8Helped
Add proposal cluster learning (ablation)RINet · DIOR trainval and testingmAP: 26.6 -> 27.1Helped
Add proposal cluster learning (ablation)RINet · DIOR trainval and testingCorLoc: 48.8 -> 51.4Helped
Add complementary multiple instance mining (ablation)RINet · DIOR trainval and testingmAP: 26.6 -> 28.3Helped
Add complementary multiple instance mining (ablation)RINet · DIOR trainval and testingCorLoc: 48.8 -> 52.8Helped

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