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

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Semantic Segmentation On Potsdam

평가 지표

mIoU

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	mIoU	Paper Title	Repository
SA-Gate	84.28	Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation	-
UnetFormer	85.18	UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery	-
PSPNet	82.98	Pyramid Scene Parsing Network	-
FPN	82.99	Feature Pyramid Networks for Object Detection	-
LMFNet-3	86.39	LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing	-
SegFormer-B1	84.37	SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers	-
LMFNet-2	85.51	LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing	-
SegFormer-B0	83.67	SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers	-
SegFormer-B2	84.65	SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers	-
CMX	85.97	CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers	-
V-FuseNet	84.36	Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks	-

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Semantic Segmentation On Potsdam | SOTA | HyperAI초신경