Semantic Segmentation On Eventscape

Metriken

mIoU

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
CMX (B4)	64.28	CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
CMX (B2)	61.90	CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
SegFormer-B4	59.86	SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
SegFormer-B2	58.69	SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
SA-Gate	53.94	Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation
DeepLabV3+	53.65	Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Trans4Trans	51.86	Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World
CGNet	44.75	CGNet: A Light-weight Context Guided Network for Semantic Segmentation
Fast-SCNN	44.27	Fast-SCNN: Fast Semantic Segmentation Network
ISSAFE	43.61	ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data
RFNet	41.34	Real-time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-driving Images
SwiftNet	36.67	In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images

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Semantic Segmentation On Eventscape | SOTA | HyperAI