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Research map / Detection and segmentation / CNN features

Neural Outlier Rejection for Self-Supervised Keypoint Learning

ICLR 2020 · Detection and segmentation › CNN features · approach: Local features and context in CNNs · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Enable cross-border keypoint detection (ablation)KeyPointNet · HPatches, 240x320Repeatability: 0.633 -> 0.689Helped
Enable cross-border keypoint detection (ablation)KeyPointNet · HPatches, 240x320M.Score: 0.491 -> 0.537Helped
Add descriptor upsampling (ablation)KeyPointNet with cross-border detection · HPatches, 240x320Cor-1: 0.491 -> 0.579Helped
Add descriptor upsampling (ablation)KeyPointNet with cross-border detection · HPatches, 240x320Cor-3: 0.805 -> 0.866Helped
Train descriptors using L_IO without L_desc (ablation)KeyPointNet with descriptor upsampling · HPatches, 240x320Cor-1: 0.579 -> 0.602Helped
Train descriptors using L_IO without L_desc (ablation)KeyPointNet with descriptor upsampling · HPatches, 240x320Cor-3: 0.866 -> 0.836Hurt
Add triplet descriptor loss L_desc (ablation)V3 - IO-Net · HPatches, 240x320Cor-3: 0.836 -> 0.867Helped
Add triplet descriptor loss L_desc (ablation)V3 - IO-Net · HPatches, 240x320Cor-1: 0.602 -> 0.591Hurt
Use the proposed self-supervised keypoint method instead of UnsuperPointHPatches · HPatches, 240x320Repeatability: 0.645 -> 0.686Helped
Use the proposed self-supervised keypoint method instead of UnsuperPointHPatches · HPatches, 240x320, 300 pointsM.Score: 0.424 -> 0.544Helped

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