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A Causal Debiasing Framework for Unsupervised Salient Object Detection

AAAI 2022 · Detection and segmentation › Open-set and few-shot detection · approach: Unsupervised object and part discovery · 34 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Apply causal debiasing with DSR pseudo-labelsStrong baseline USOD model · DUTS-TEEξ: .843 -> .855Helped
Apply causal debiasing with DSR pseudo-labelsStrong baseline USOD model · DUTS-TEFβ: .747 -> .764Helped
Apply causal debiasing with DSR pseudo-labelsStrong baseline USOD model · DUTS-TEM: .070 -> .064Helped
Add explicit low- and high-level visual contrast fusion (ablation)Vanilla USOD baseline · DUTS-TEEξ: .787 -> .843Helped
Add causal de-confounding to the contrast fusion block (ablation)Contrast Fusion Block model · DUTS-TEEξ: .843 -> .844Helped
Add image-level weighting based on spatial distribution bias (ablation)De-confounded USOD model · DUTS-TEEξ: .844 -> .855Helped
Use res3 rather than res2 as the low-level contrast feature (ablation)DUTS DCFD with ResNet-50 · DUTS-TEEξ: .855 -> .856No clear effect
Use res5 rather than res4 as the high-level contrast feature (ablation)DUTS DCFD with ResNet-50 · DUTS-TEEξ: .840 -> .855Helped
Train on DUTS rather than MSRA-B (ablation)Causal debiasing framework · DUTS-TEEξMixed
Use DCFD instead of EDNSUnsupervised salient object detection · DUTS-TEEξ: .847 -> .855Helped

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