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TVConv: Efficient Translation Variant Convolution for Layout-Aware Visual Processing

CVPR 2022 · Architectures and efficiency › CNN design · approach: Dense matching and estimation CNNs · 41 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace depthwise convolution with TVConvMobileNetV2 on LFW · LFWcomputational cost: 3.05xHelped
Use TVConv in MBx0.5 rather than the original MBx1.0MobileNetV2 on LFW · LFWthroughput: 2.3xHelped
Replace depthwise convolution with TVConvMobileNetV2 MBx0.1 · Face verification datasetsmean accuracy: 4.21%Helped
Use TVConv instead of the depthwise-convolution variantDeepLabV3+ optic disc/cup segmentation · Unseen domains, meanDSC: 86.10 -> 87.31Helped
Use TVConv instead of the depthwise-convolution variantDeepLabV3+ optic disc/cup segmentation · Unseen domains, meanHD: 26.26 -> 24.32Helped
Initialize affinity maps from data statistics rather than constant 1 (ablation)TVConv · LFW, MBx0.1/0.5/1.0error (%): 2.84 ± 0.11 -> 2.92 ± 0.22; 1.73 ± 0.07 -> 1.75 ± 0.18; 1.43 ± 0.12 -> 1.62 ± 0.20Hurt
Increase weight-generating block width to 128 channels from the default 64 (ablation)TVConv MBx0.2 · LFWerror (%): 2.31 ± 0.12 -> 2.60 ± 0.10Hurt
Reduce weight-generating block width from 64 to 32 or 8 channels (ablation)TVConv MBx0.2 · LFWerror (%): 2.31 ± 0.12 -> 2.65 ± 0.21; 2.31 ± 0.12 -> 2.65 ± 0.12Hurt
Increase weight-generating block depth from 3 to 4 layers (ablation)TVConv MBx0.2 · LFWerror (%): 2.31 ± 0.12 -> 2.49 ± 0.16Hurt
Reduce weight-generating block depth from 3 to 2 or 1 layer (ablation)TVConv MBx0.2 · LFWerror (%): 2.31 ± 0.12 -> 2.44 ± 0.15; 2.31 ± 0.12 -> 2.48 ± 0.27Mixed
Increase affinity-map channels from 4 to 8 (ablation)TVConv MBx0.2 · LFWerror (%): 2.31 ± 0.12 -> 2.49 ± 0.16Hurt
Reduce affinity-map channels from 4 to 2 or 1 (ablation)TVConv MBx0.2 · LFWerror (%): 2.31 ± 0.12 -> 2.44 ± 0.19; 2.31 ± 0.12 -> 2.52 ± 0.15Hurt

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