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Rgb D Salient Object Detection On Stere

Métriques

Average MAE

S-Measure

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Average MAE	S-Measure	Paper Title	Repository
UC-Net	0.039	90.3	UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
DASNet	0.037	91.0	Is Depth Really Necessary for Salient Object Detection?
CoLANet	0.039	90.8	CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection
BTS-Net	0.038	91.5	BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection
UCNet-CVAE	0.039	89.8	Uncertainty Inspired RGB-D Saliency Detection
JL-DCF	0.042	90.5	JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
BBS-Net	0.041	90.8	Bifurcated backbone strategy for RGB-D salient object detection
JL-DCF*	0.039	91.1	Siamese Network for RGB-D Salient Object Detection and Beyond
D3Net	0.046	89.9	Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
CPFP	0.051	87.9	Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection
SPSN	0.035	90.7	SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
DFormer-L	0.030	92.3	DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
BiANet	0.043	90.4	Bilateral Attention Network for RGB-D Salient Object Detection
UCNet-ABP	0.037	90.4	Uncertainty Inspired RGB-D Saliency Detection

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