Rgb D Salient Object Detection On Sip

Métriques

Average MAE

S-Measure

Résultats

Résultats de performance de divers modèles sur ce benchmark

			Paper Title
CPFP	0.064	85.0	Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection
D3Net	0.063	86.0	Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
PGAR	0.059	87.5	Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection
BBS-Net	0.055	87.9	Bifurcated backbone strategy for RGB-D salient object detection
BiANet	0.052	88.3	Bilateral Attention Network for RGB-D Salient Object Detection
UC-Net	0.051	87.5	UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
JL-DCF	0.051	87.9	JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
UCNet-ABP	0.049	87.6	Uncertainty Inspired RGB-D Saliency Detection
JL-DCF*	0.046	89.2	Siamese Network for RGB-D Salient Object Detection and Beyond
UCNet-CVAE	0.045	88.3	Uncertainty Inspired RGB-D Saliency Detection
BTS-Net	0.044	89.6	BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection
DDNet	0.043	-	Densely Deformable Efficient Salient Object Detection Network
SPSN	0.042	89.2	SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
CoLANet	0.042	89.5	CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection
VST	0.040	90.4	Visual Saliency Transformer
DFormer-L	0.032	91.5	DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation

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Rgb D Salient Object Detection On Sip | SOTA | HyperAI