HyperAI초신경

홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

한국어

HyperAI초신경

Rgb D Salient Object Detection On Sip

평가 지표

Average MAE

S-Measure

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Average MAE	S-Measure	Paper Title	Repository
PGAR	0.059	87.5	Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection
VST	0.040	90.4	Visual Saliency Transformer
UCNet-CVAE	0.045	88.3	Uncertainty Inspired RGB-D Saliency Detection
DFormer-L	0.032	91.5	DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
UCNet-ABP	0.049	87.6	Uncertainty Inspired RGB-D Saliency Detection
BiANet	0.052	88.3	Bilateral Attention Network for RGB-D Salient Object Detection
SPSN	0.042	89.2	SPSN: Superpixel Prototype Sampling 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
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
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
BBS-Net	0.055	87.9	Bifurcated backbone strategy for RGB-D salient object detection
JL-DCF*	0.046	89.2	Siamese Network for RGB-D Salient Object Detection and Beyond
JL-DCF	0.051	87.9	JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
CoLANet	0.042	89.5	CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection

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