Rgb D Salient Object Detection On Stere

Métriques

Average MAE

S-Measure

Résultats

Résultats de performance de divers modèles sur ce benchmark

			Paper Title
CPFP	0.051	87.9	Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection
D3Net	0.046	89.9	Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
BiANet	0.043	90.4	Bilateral Attention Network for RGB-D Salient Object 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
UC-Net	0.039	90.3	UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
CoLANet	0.039	90.8	CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection
UCNet-CVAE	0.039	89.8	Uncertainty Inspired RGB-D Saliency Detection
JL-DCF*	0.039	91.1	Siamese Network for RGB-D Salient Object Detection and Beyond
BTS-Net	0.038	91.5	BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection
DASNet	0.037	91.0	Is Depth Really Necessary for Salient Object Detection?
UCNet-ABP	0.037	90.4	Uncertainty Inspired RGB-D Saliency 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

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Rgb D Salient Object Detection On Stere | SOTA | HyperAI