HyperAI초신경

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

한국어

HyperAI초신경

Rgb D Salient Object Detection On Nlpr

평가 지표

Average MAE

S-Measure

max E-Measure

max F-Measure

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Average MAE	S-Measure	max E-Measure	max F-Measure	Paper Title	Repository
D3Net	0.030	91.2	95.3	89.7	Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
DFormer-L	0.016	94.2	97.1	93.9	DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
BBS-Net	0.023	93.0	96.1	91.8	Bifurcated backbone strategy for RGB-D salient object detection
UCNet-CAVE	0.025	91.7	-	-	Uncertainty Inspired RGB-D Saliency Detection
BiANet	0.024	92.5	96.1	91.4	Bilateral Attention Network for RGB-D Salient Object Detection
CoLANet	0.021	93.5	95.7	90.9	CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection
DASNet	0.021	92.9	-	92.9	Is Depth Really Necessary for Salient Object Detection?
JL-DCF*	0.023	92.6	96.4	91.7	Siamese Network for RGB-D Salient Object Detection and Beyond
VST	-	0.932	-	-	Visual Saliency Transformer
JL-DCF	0.022	92.5	96.2	91.6	JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
UCNet-ABP	0.024	91.9	-	-	Uncertainty Inspired RGB-D Saliency Detection
SPSN	0.022	92.6	96.2	91.4	SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
UC-Net	0.025	92.0	-	-	UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
CPFP	0.036	88.8	93.2	86.7	Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection

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