Few Shot Semantic Segmentation On Fss 1000 5

평가 지표

Mean IoU

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이 벤치마크에서 각 모델의 성능 결과

모델 이름
Mean IoU
Paper TitleRepository
HSNet (HM, ResNet-101)88.5HM: Hybrid Masking for Few-Shot Segmentation-
HSNet (HM, ResNet-50)88HM: Hybrid Masking for Few-Shot Segmentation-
HSNet (ResNet-50)87.8Hypercorrelation Squeeze for Few-Shot Segmentation-
SegGPT (ViT)89.3SegGPT: Segmenting Everything In Context-
VAT (ResNet-50)90.7Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation-
VAT (HM, ResNet-50)89.9HM: Hybrid Masking for Few-Shot Segmentation-
EfficientLab + PRN-PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization-
Annotation-free FSS (Without Annotation,ResNet-50)86.8Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach-
HSNet (VGG-16)85.8Hypercorrelation Squeeze for Few-Shot Segmentation-
DACM (ResNet-50)91.6Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation-
DCAMA (Swin-B)90.4Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation-
DACM (ResNet-101)91.7Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation-
VAT (HM, ResNet-101)90.5HM: Hybrid Masking for Few-Shot Segmentation-
FSS-1000 (VGG-16)80.12FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation-
GF-SAM88.9Bridge the Points: Graph-based Few-shot Segment Anything Semantically-
VAT90.6Cost Aggregation Is All You Need for Few-Shot Segmentation-
HSNet (ResNet-101)88.5Hypercorrelation Squeeze for Few-Shot Segmentation-
SSP88.6Self-Support Few-Shot Semantic Segmentation-
VAT (ResNet-101)90.8Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation-
DCAMA (ResNet-50)88.8Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation-
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Few Shot Semantic Segmentation On Fss 1000 5 | SOTA | HyperAI초신경