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Designing Effective Inter-Pixel Information Flow for Natural Image Matting

CVPR 2017 · Image generation › Autoregressive and latent generation · approach: Classical generative image synthesis · 190 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace Gastal and Oliveira regularization with proposed information-flow regularizationKL-D · Alpha matting benchmark, overallSAD: 24.4 %Helped
Replace Gastal and Oliveira regularization with proposed information-flow regularizationKL-D · Alpha matting benchmark, overallMSE: 28.5 %Helped
Replace Gastal and Oliveira regularization with proposed information-flow regularizationSM · Alpha matting benchmark, overallSAD: 6.0 %Helped
Replace Gastal and Oliveira regularization with proposed information-flow regularizationSM · Alpha matting benchmark, overallMSE: 13.6 %Helped
Replace Gastal and Oliveira regularization with proposed information-flow regularizationCS · Alpha matting benchmark, overallSAD: 4.9 %Helped
Replace Gastal and Oliveira regularization with proposed information-flow regularizationCS · Alpha matting benchmark, overallMSE: 18.7 %Helped
Remove color-mixture flow (ablation)Proposed matting method · Natural image matting, wide opacity gradientsmatte qualityHurt
Remove direct K-to-U information flow (ablation)Proposed matting method · Natural image matting, regions around holesmatte qualityHurt
Remove confidence weighting from K-to-U flow (ablation)Proposed matting method · Natural image matting, similar foreground and background colorsmatte qualityHurt
Remove intra-unknown-region information flow (ablation)Proposed matting method · Natural image matting, intricate structuresmatte qualityHurt
Remove local information flow (ablation)Proposed matting method · Natural image mattingspatial coherenceHurt
Include K-to-U information flow for highly transparent objects (ablation)Proposed matting method · Highly transparent foreground objectsmatte qualityHurt

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