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Research map / Detection and segmentation / Weakly supervised detection and segmentation

Attention-Based Dropout Layer for Weakly Supervised Object Localization

CVPR 2019 · Detection and segmentation › Weakly supervised detection and segmentation · approach: Weakly supervised object localization · 398 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add ADLVGG-GAP · CUB-200-2011Top-1 Loc: 34.41 -> 52.36Helped
Add ADLResNet50-SE · CUB-200-2011Top-1 Loc: 42.72 -> 62.29Helped
Add ADLInceptionV3 · ImageNet-1kTop-1 Loc: 46.29* -> 48.71Helped
Add ADLVGG-GAP · ImageNet-1kTop-1 Loc: 45.83* -> 44.92Hurt
Use a 75% drop rate rather than applying the drop mask every iteration (ablation)ADL on VGG-GAP · CUB-200-2011Top-1 Loc: 44.11 -> 49.69Helped
Set drop rate to 0% rather than 25% (ablation)ADL on VGG-GAP · CUB-200-2011Top-1 Loc: 47.98 -> 32.24Hurt
Add an importance map alongside the drop mask (ablation)ADL on VGG-GAP · CUB-200-2011Top-1 Loc: 47.67 -> 49.69Helped
Add a drop mask alongside the importance map (ablation)ADL on VGG-GAP · CUB-200-2011Top-1 Loc: 33.91 -> 49.69Helped
Add ADL to pool4 and pool3 after conv5-3 (ablation)ADL on VGG-GAP · CUB-200-2011Top-1 Loc: 41.73 -> 48.02; 48.02 -> 52.36Helped
Add ADL to lower-level pool2 after pool3 (ablation)ADL on VGG-GAP · CUB-200-2011Top-1 Loc: 52.36 -> 48.46Hurt
Add ADL to pool1 after pool2 (ablation)ADL on VGG-GAP · CUB-200-2011Top-1 Loc: 48.46 -> 49.69Helped

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