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Progressive One-shot Human Parsing

AAAI 2021 · Label-efficient and robust learning › Zero-/few-shot and meta-learning · approach: Fine-tuning strategies for few-shot · 17 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use POPNet instead of the strongest baselineATR-OS k-way · ATR-OS k-wayNovel Class MIoU (Mean): 13.0 -> 21.8Helped
Use POPNet instead of SG-OneATR-OS k-way · ATR-OS k-wayHuman MIoU (Mean): 44.4 -> 60.5Helped
Use POPNet instead of AMPATR-OS one-way · ATR-OS one-wayNovel Class MIoU (Mean): 8.8 -> 27.7Helped
Use POPNet instead of SG-OneATR-OS one-way · ATR-OS one-wayHuman MIoU (Mean): 39.8 -> 52.8Helped
Use AGM and NCM together instead of either module alone (ablation)POPNet on ATR-OS Fold 1 · ATR-OS Fold 1Novel Class MIoU: 1.0, 3.5 -> 15.1Helped
Use AGM and NCM together instead of either module alone (ablation)POPNet on ATR-OS Fold 1 · ATR-OS Fold 1Human MIoU: 40.2, 40.4 -> 47.9Helped
Add weight shifting to DML (ablation)POPNet on ATR-OS Fold 1 · ATR-OS Fold 1Novel Class MIoU: 15.1 -> 18.6Helped
Add weight shifting to DML (ablation)POPNet on ATR-OS Fold 1 · ATR-OS Fold 1Human MIoU: 47.9 -> 47.1Hurt
Add progressive parsing with KIM (ablation)POPNet on ATR-OS Fold 1 · ATR-OS Fold 1Novel Class MIoU: 18.6 -> 20.0Helped
Add progressive parsing with KIM (ablation)POPNet on ATR-OS Fold 1 · ATR-OS Fold 1Human MIoU: 47.1 -> 52.5Helped
Add dynamic prototype generation (ablation)POPNet on ATR-OS Fold 1 · ATR-OS Fold 1Novel Class MIoU: 20.0 -> 24.1Helped
Add dynamic prototype generation (ablation)POPNet on ATR-OS Fold 1 · ATR-OS Fold 1Human MIoU: 52.5 -> 60.6Helped

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