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Research map / Detection and segmentation / CNN features

Robust Deep Co-Saliency Detection with Group Semantic

AAAI 2019 · Detection and segmentation › CNN features · approach: RGB salient object detection · 79 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add co-category supervision (lambda 0 to 1) (ablation)H-LRBP model · COCO-SEG testSα: 0.7413 -> 0.7578Helped
add co-category supervision (lambda 0 to 1) (ablation)AP model · COCO-SEG testFβ: 0.6430 -> 0.6994Helped
add co-category supervision (lambda 0 to 1) (ablation)H-LRBP model · COCO-SEG testMAE: 0.1048 -> 0.0927Helped
replace FC+average pooling with H-LRBP (lambda=0) (ablation)multi-scale model · COCO-SEG testSα: 0.7070 -> 0.7413Helped
replace FC+average pooling with H-LRBP (lambda=0) (ablation)multi-scale model · COCO-SEG testFβ: 0.6430 -> 0.6848Helped
replace average pooling with H-LRBP (lambda=1) (ablation)multi-scale model · COCO-SEG testAUC: 0.9317 -> 0.9398Helped
use multi-scale instead of single-scale features (H-LRBP, lambda=0) (ablation)H-LRBP model · COCO-SEG testSα: 0.6951 -> 0.7413Helped
use multi-scale instead of single-scale features (AP, lambda=0) (ablation)AP model · COCO-SEG testMAE: 0.1245 -> 0.1217Helped
use group-wise pooling over images instead of GW concatenation (ablation)single-scale, lambda=0 · COCO-SEG testSα: 0.6424 -> 0.6951Helped
proposed method vs GWfull model · Cosal2015SαHelped

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