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CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detection

CVPR 2019 · Detection and segmentation › CNN features · approach: RGB salient object detection · 133 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add captioning semantics to local-global saliency prediction (ablation)CapSal LGPN · COCO-CapSalFβ: 0.821 -> 0.860Helped
add captioning semantics to local-global saliency prediction (ablation)CapSal LGPN · DUTS-testFβ: 0.717 -> 0.756Helped
use local and global perception together instead of either module alone (ablation)CapSal without captioning · COCO-CapSalFβ: 0.813 -> 0.821; 0.786 -> 0.821Helped
add joint training of ICN and LGPN (ablation)CapSal · COCO-CapSalFβ: 0.843 -> 0.860Helped
add joint training of ICN and LGPN (ablation)CapSal · DUTS-testFβ: 0.720 -> 0.756Helped
add textual attention to the caption embedding (ablation)CapSal ICN · COCO-CapSalFβ: 0.837 -> 0.860Helped
add textual attention to the caption embedding (ablation)CapSal ICN · DUTS-testFβ: 0.725 -> 0.756Helped
use ground-truth captions instead of generated captions (ablation)CapSal LGPN · COCO-CapSalFβ: 0.860 -> 0.866Helped
train CapSal on DUTS-train rather than COCO-CapSalCapSal · DUTS-testFβ: 0.756 -> 0.789Helped
train CapSal on COCO-CapSal rather than DUTS-trainCapSal · COCO-CapSalFβ: 0.815 -> 0.860Helped
use CapSal trained on DUTS-train instead of the strongest baselineCapSal · PASCAL-SMAE: 0.072 -> 0.064Helped
use CapSal trained on DUTS-train instead of the strongest baselineCapSal · HKU-ISFβ: 0.895 -> 0.878Hurt

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