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Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation

CVPR 2019 · Probabilistic and scientific modeling › Molecules and proteins · approach: Flow matching generative modeling · 304 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add shared-weight IRR (ablation)PWC-Net · Sintel Clean trainingEPE: 3.13 -> 2.79Helped
Add occlusion estimation (ablation)PWC-Net · FlyingChairs FullEPE: 2.03 -> 1.94Helped
Add bidirectional flow estimation alone (ablation)PWC-Net · FlyingChairs FullEPE: 2.03 -> 2.06Hurt
Add IRR, bidirectional flow, and occlusion estimation together (ablation)PWC-Net · Sintel Clean trainingEPE: 3.13 -> 2.69Helped
Add bilateral refinement (ablation)PWC-Net with IRR, bidirectional flow, and occlusion estimation · Sintel Clean trainingEPE: 2.69 -> 2.32Helped
Add occlusion upsampling (ablation)PWC-Net with bilateral refinement · Sintel Clean trainingocclusion F1-score: 0.648 -> 0.677Helped
Add occlusion upsampling (ablation)PWC-Net with bilateral refinement · Sintel Clean trainingEPE: 2.32 -> 2.34Hurt
Replace the proposed upsampling layer with FlowNet2's refinement network (ablation)PWC-Net with IRR, bidirectional flow, occlusion estimation, and bilateral refinement · Sintel Clean trainingEPE: 2.32 -> 2.90Hurt
Use more shared-weight IRR iterations instead of stacking separate FlowNetS networks (ablation)FlowNetS · Sintel CleanEPE: 3.303 vs 3.391Helped
Use the full IRR-PWC designPWC-Net · Sintel Final testEPE: 5.04 -> 4.58Helped

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