Few Shot Semantic Segmentation On Pascal 5I 5

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Mean IoU

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이 벤치마크에서 각 모델의 성능 결과

		Paper Title
SegGPT (ViT)	89.8	SegGPT: Segmenting Everything In Context
GF-SAM	82.6	Bridge the Points: Graph-based Few-shot Segment Anything Semantically
PGMA-Net (ResNet-101)	78.6	Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyond
FPTrans (DeiT-B/16)	78	Feature-Proxy Transformer for Few-Shot Segmentation
DGPNet (ResNet-101)	75.4	Dense Gaussian Processes for Few-Shot Segmentation
PGMA-Net (ResNet-50)	75.2	Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyond
DCAMA (Swin-B)	74.9	Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation
PGMA-Net (ViT-B/16)	74.6	Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyond
AENet (ResNet-50)	74.2	Eliminating Feature Ambiguity for Few-Shot Segmentation
HMNet (ResNet-50)	74.1	Hybrid Mamba for Few-Shot Segmentation
MSANet (ResNet-101)	73.99	MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentation
FPTrans (ViT-B/16)	73.7	Feature-Proxy Transformer for Few-Shot Segmentation
DGPNet (ResNet-50)	73.5	Dense Gaussian Processes for Few-Shot Segmentation
DACM (VAT, ResNet-101)	73.3	Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation
SSP (ResNet-101)	73.1	Self-Support Few-Shot Semantic Segmentation
MSANet (ResNet-50)	72.6	MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentation
MSHNet	72.3	Multi-similarity based Hyperrelation Network for few-shot segmentation
BAM (SVF, ResNet-50)	72.28	Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
VAT (ResNet-101)	72	Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation
MIANet (VGG-16)	71.99	MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

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Few Shot Semantic Segmentation On Pascal 5I 5

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