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Rgb D Salient Object Detection On Des

Métriques

Average MAE

S-Measure

max E-Measure

max F-Measure

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Average MAE	S-Measure	max E-Measure	max F-Measure	Paper Title	Repository
CoLANet	0.018	93.5	96.3	92.5	CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection
CPFP	0.038	87.2	92.3	84.6	Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection
DFormer-L	0.013	94.8	98.0	95.6	DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
BBS-Net	0.021	93.3	96.6	92.7	Bifurcated backbone strategy for RGB-D salient object detection
UC-Net	0.019	93.4	-	-	UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
BiANet	0.021	93.1	97.1	92.6	Bilateral Attention Network for RGB-D Salient Object Detection
SPSN	0.016	93.8	97.6	94.3	SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
DASNet	0.023	90.8	-	92.8	Is Depth Really Necessary for Salient Object Detection?
JL-DCF*	0.021	93.6	97.5	92.9	Siamese Network for RGB-D Salient Object Detection and Beyond
UCNet-ABP	0.016	94.0	-	-	Uncertainty Inspired RGB-D Saliency Detection
BTS-Net	0.018	94.3	97.9	94.0	BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection
JL-DCF	0.022	92.9	96.8	91.9	JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
UCNet-CVAE	0.016	93.7	-	-	Uncertainty Inspired RGB-D Saliency Detection

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