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Convolutional Hough Matching Networks

CVPR 2021 · Architectures and efficiency › CNN design · approach: Filter design and trained-filter analysis · 59 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace CHM with global RHM voting (ablation)CHMNet · SPair-71kPCK (α_bbox=0.1): 46.3 -> 38.2Hurt
remove all CHM layers (ablation)CHMNet · SPair-71kPCK (α_bbox=0.1): 46.3 -> 21.6Hurt
remove the last 4D CHM layer (ablation)CHMNet · SPair-71kPCK (α_bbox=0.1): 46.3 -> 43.1Hurt
remove Gaussian kernel G from flow formation (ablation)CHMNet · SPair-71kPCK (α_bbox=0.1): 46.3 -> 45.5Hurt
remove the soft keypoint sampler (ablation)CHMNet · SPair-71kPCK (α_bbox=0.1): 46.3 -> 43.8Hurt
replace position-sensitive isotropic kernels with unshared full kernels (ablation)CHMNet · SPair-71kPCK (α_bbox=0.1): 46.4 -> 44.8Hurt
replace position-sensitive isotropic kernels with fully isotropic kernels (ablation)CHMNet · SPair-71kPCK (α_bbox=0.1): 46.4 -> 44.9Hurt
extend the position-sensitive voting space from 4D to 6D to include scale (ablation)CHMNet · SPair-71kPCK (α_bbox=0.1): 44.5 -> 46.4Helped
use sparse correlations with K nearest neighbors (ablation)CHMNet · SPair-71kPCK (α_bbox=0.1): 46.4 -> 45.2No clear effect
increase the number of CHM kernels (channel size) (ablation)CHMNet · PF-PASCAL and SPair-71kPCKMixed

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