SOTAVerified

Few-Shot Semantic Segmentation

Few-shot semantic segmentation (FSS) learns to segment target objects in query image given few pixel-wise annotated support image.

Papers

Showing 51–75 of 168 papers

TitleStatusHype
PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior MaskCode1
A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot SegmentationCode1
Learning Meta-class Memory for Few-Shot Semantic SegmentationCode1
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight TransformerCode1
Few-Shot Segmentation via Cycle-Consistent TransformerCode1
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic SegmentationCode1
Adaptive Prototype Learning and Allocation for Few-Shot SegmentationCode1
Hypercorrelation Squeeze for Few-Shot SegmentationCode1
Self-Guided and Cross-Guided Learning for Few-Shot SegmentationCode1
Mining Latent Classes for Few-shot SegmentationCode1
Harmonic Feature Activation for Few-Shot Semantic SegmentationCode1
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?Code1
Prototype-based Incremental Few-Shot Semantic SegmentationCode1
Generalized Few-shot Semantic SegmentationCode1
Prototype Mixture Models for Few-shot Semantic SegmentationCode1
Prior Guided Feature Enrichment Network for Few-Shot SegmentationCode1
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without AnnotationCode1
Part-aware Prototype Network for Few-shot Semantic SegmentationCode1
Objectness-Aware Few-Shot Semantic SegmentationCode1
On the Texture Bias for Few-Shot CNN SegmentationCode1
PANet: Few-Shot Image Semantic Segmentation with Prototype AlignmentCode1
Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation—0
DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining—0
FSSUWNet: Mitigating the Fragility of Pre-trained Models with Feature Enhancement for Few-Shot Semantic Segmentation in Underwater ImagesCode0
Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SegGPT (ViT)Mean IoU83.2—Unverified
2PGMA-Net (ResNet-101)Mean IoU77.6—Unverified
3DCAMA (ResNet-101)FB-IoU77.6—Unverified
4PGMA-Net (ResNet-50)Mean IoU74.1—Unverified
5PGMA-Net (ViT-B/16)Mean IoU74.1—Unverified
6GF-SAM (DINOv2)Mean IoU72.1—Unverified
7HMNet (ResNet-50)Mean IoU70.4—Unverified
8AENet (ResNet-50)Mean IoU70.3—Unverified
9HDMNet (DifFSS, ResNet-50)Mean IoU70.2—Unverified
10VAT + MSI (ResNet-101)Mean IoU70.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SegGPT (ViT)Mean IoU89.8—Unverified
2GF-SAM (DINOv2)Mean IoU82.6—Unverified
3PGMA-Net (ResNet-101)Mean IoU78.6—Unverified
4FPTrans (DeiT-B/16)Mean IoU78—Unverified
5DGPNet (ResNet-101)Mean IoU75.4—Unverified
6PGMA-Net (ResNet-50)Mean IoU75.2—Unverified
7DCAMA (Swin-B)Mean IoU74.9—Unverified
8PGMA-Net (ViT-B/16)Mean IoU74.6—Unverified
9AENet (ResNet-50)Mean IoU74.2—Unverified
10HMNet (ResNet-50)Mean IoU74.1—Unverified