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Learning from the Target: Dual Prototype Network for Few Shot Semantic Segmentation

AAAI 2022 · Detection and segmentation › Open-set and few-shot detection · approach: Few-shot segmentation · 17 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add the dual-prototype modules to PFENetPFENet · PASCAL-5i 1-shotmIoU: 60.8 -> 62.7Helped
Add the dual-prototype modules to PFENetPFENet · PASCAL-5i 5-shotmIoU: 61.9 -> 66.2Helped
Use DPNet instead of the strongest listed baselinePASCAL-5i · PASCAL-5i 1-shotmIoU: 59.7 -> 62.7Helped
Use DPNet instead of the strongest listed baselinePASCAL-5i · PASCAL-5i 5-shotmIoU: 66.6 -> 66.2Hurt
Use DPNet instead of the strongest listed baselineCOCO-20i · COCO-20i 1-shotmIoU: 37.0 -> 37.2Helped
Use DPNet instead of the strongest listed baselineCOCO-20i · COCO-20i 5-shotmIoU: 42.5 -> 42.9Helped
Add PIM to the baseline network (ablation)DPNet · PASCAL-5i 1-shotmIoU: 53.8 -> 55.5Helped
Add MSF after PIM (ablation)DPNet · PASCAL-5i 1-shotmIoU: 55.5 -> 56.0Helped
Add CCM to the PIM and MSF network (ablation)DPNet · PASCAL-5i 1-shotmIoU: 56.0 -> 58.0Helped
Replace cycle-consistent foreground selection with global query-image average features (ablation)DPNet · PASCAL-5i 1-shotmIoU: 58.0 -> 56.5Hurt
Replace cycle-consistent foreground selection with cosine-similarity selection (ablation)DPNet · PASCAL-5i 1-shotmIoU: 58.0 -> 57.3Hurt
Use multi-scale inference (ablation)DPNet · PASCAL-5i 1-shotmIoU: 58.0 -> 58.7Helped

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