Semantic Segmentation On Urbanlf

Métriques

mIoU (Real)

mIoU (Syn)

Résultats

Résultats de performance de divers modèles sur ce benchmark

			Paper Title
CMNeXt (RGB-LF8)	83.22	80.74	Delivering Arbitrary-Modal Semantic Segmentation
CMNeXt (RGB-LF80)	83.11	81.02	Delivering Arbitrary-Modal Semantic Segmentation
CMNeXt (RGB-LF33)	82.62	80.98	Delivering Arbitrary-Modal Semantic Segmentation
SegFormer	82.20	78.53	SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
OCR (HRNetV2-W48)	78.60	79.36	U-Net: Convolutional Networks for Biomedical Image Segmentation
SETR (ViT-Large)	77.74	77.69	Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
TMANet	77.14	76.41	Temporal Memory Attention for Video Semantic Segmentation
TDNet (ResNet-50)	76.48	74.71	Temporally Distributed Networks for Fast Video Semantic Segmentation
PSPNet	76.34	75.78	Pyramid Scene Parsing Network
DeepLabV3+ (ResNet-101)	76.27	-	Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
DAVSS	75.91	74.27	Video Semantic Segmentation with Distortion-Aware Feature Correction
SA-Gate	n.a.	79.53	Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation
ESANet	n.a.	79.43	Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis
MTINet (HRNetV2-W48)	n.a.	79.10	MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning

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Semantic Segmentation On Urbanlf | SOTA | HyperAI