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Shortlist Selection With Residual-Aware Distance Estimator for K-Nearest Neighbor Search

CVPR 2016 · Architectures and efficiency › RNNs and normalization · approach: Dataset condensation and flow estimation · 11 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add residual-aware shortlist selection to the inverted indexII · GIST384-80M, K=100, shortlist 1Mrecall: 0.837 -> 0.890Helped
Add residual-aware shortlist selection to the inverted multi-indexIMI · GIST384-80M, K=100, shortlist 1Mrecall: 0.949 -> 0.976Helped
Add residual-aware shortlist selection to the inverted indexII · SIFT-1B, K=100, shortlist 1Mrecall: 0.872 -> 0.891Helped
Add residual-aware shortlist selection to the inverted multi-indexIMI · SIFT-1B, K=100, shortlist 1Mrecall: 0.994 -> 0.992Hurt
Add residual-aware shortlist selection to the inverted indexII · S-VLAD2K-1B, K=100, shortlist 1Mrecall: 0.057 -> 0.082Helped
Add residual-aware shortlist selection to the inverted multi-indexIMI · S-VLAD2K-1B, K=100, shortlist 1Mrecall: 0.158 -> 0.182Helped
Add the residual-aware estimator to IMI shortlist selectionIMI, VLAD8K-1M · VLAD8K-1Mshortlist computation time: 3.43, 3.54 -> 1.92, 2.75Helped
Train αK using sampled true neighbors and sampled non-neighbors instead of using default αK=1 (ablation)Residual-aware distance estimator · Benchmarks tested in the papershortlist accuracy: up to 20%Helped
Change the IMI residual-distance partition count from P=2 to P=4 (ablation)IMI+Ours and OIMI+Ours · Benchmarks tested in the papershortlist accuracyMixed

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