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 51100 of 168 papers

TitleStatusHype
Hypercorrelation Squeeze for Few-Shot SegmentationCode1
A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation ModelsCode1
Integrative Few-Shot Learning for Classification and SegmentationCode1
Cost Aggregation Is All You Need for Few-Shot SegmentationCode1
Intermediate Prototype Mining Transformer for Few-Shot Semantic SegmentationCode1
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot SegmentationCode1
Iterative Few-shot Semantic Segmentation from Image Label TextCode1
Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather ConditionsCode1
Cross-Domain Few-Shot Semantic SegmentationCode1
Learnable Prompt for Few-Shot Semantic Segmentation in Remote Sensing DomainCode1
Learning Meta-class Memory for Few-Shot Semantic SegmentationCode1
Learning Non-target Knowledge for Few-shot Semantic SegmentationCode1
Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching TransformationCode1
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic SegmentationCode1
Feature-Proxy Transformer for Few-Shot SegmentationCode1
FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware NetworkCode1
Self-Calibrated Cross Attention Network for Few-Shot SegmentationCode1
Self-Guided and Cross-Guided Learning for Few-Shot SegmentationCode1
A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot SegmentationCode1
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproachCode1
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without AnnotationCode1
Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation ModelsCode0
Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge TransferCode0
RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image SegmentationCode0
Scale-Aware Graph Neural Network for Few-Shot Semantic SegmentationCode0
Learning to Segment Medical Images from Few-Shot Sparse LabelsCode0
Quaternion-valued Correlation Learning for Few-Shot Semantic SegmentationCode0
Feature Weighting and Boosting for Few-Shot SegmentationCode0
A Language-Guided Benchmark for Weakly Supervised Open Vocabulary Semantic SegmentationCode0
AMP: Adaptive Masked Proxies for Few-Shot SegmentationCode0
Few-shot Semantic Segmentation with Self-supervision from Pseudo-classesCode0
Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot SegmentationCode0
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial LearningCode0
Clustered-patch Element Connection for Few-shot LearningCode0
Adaptive Masked Proxies for Few-Shot SegmentationCode0
A dense subgraph based algorithm for compact salient image region detectionCode0
SG-One: Similarity Guidance Network for One-Shot Semantic SegmentationCode0
Few-Shot Fruit Segmentation via Transfer LearningCode0
Foundation Model or Finetune? Evaluation of few-shot semantic segmentation for river pollutionCode0
FSS-1000: A 1000-Class Dataset for Few-Shot SegmentationCode0
FSSUWNet: Mitigating the Fragility of Pre-trained Models with Feature Enhancement for Few-Shot Semantic Segmentation in Underwater ImagesCode0
CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot LearningCode0
HM: Hybrid Masking for Few-Shot SegmentationCode0
MSI: Maximize Support-Set Information for Few-Shot SegmentationCode0
TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic SegmentationCode0
Applying ViT in Generalized Few-shot Semantic SegmentationCode0
PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise BinarizationCode0
Dual Prototypical Contrastive Learning for Few-shot Semantic SegmentationCode0
Unlocking the Potential of Pre-trained Vision Transformers for Few-Shot Semantic Segmentation through Relationship DescriptorsCode0
A Surprisingly Simple Approach to Generalized Few-Shot Semantic SegmentationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SegGPT (ViT)Mean IoU83.2Unverified
2PGMA-Net (ResNet-101)Mean IoU77.6Unverified
3DCAMA (ResNet-101)FB-IoU77.6Unverified
4PGMA-Net (ResNet-50)Mean IoU74.1Unverified
5PGMA-Net (ViT-B/16)Mean IoU74.1Unverified
6GF-SAM (DINOv2)Mean IoU72.1Unverified
7HMNet (ResNet-50)Mean IoU70.4Unverified
8AENet (ResNet-50)Mean IoU70.3Unverified
9HDMNet (DifFSS, ResNet-50)Mean IoU70.2Unverified
10VAT + MSI (ResNet-101)Mean IoU70.1Unverified
#ModelMetricClaimedVerifiedStatus
1SegGPT (ViT)Mean IoU89.8Unverified
2GF-SAM (DINOv2)Mean IoU82.6Unverified
3PGMA-Net (ResNet-101)Mean IoU78.6Unverified
4FPTrans (DeiT-B/16)Mean IoU78Unverified
5DGPNet (ResNet-101)Mean IoU75.4Unverified
6PGMA-Net (ResNet-50)Mean IoU75.2Unverified
7DCAMA (Swin-B)Mean IoU74.9Unverified
8PGMA-Net (ViT-B/16)Mean IoU74.6Unverified
9AENet (ResNet-50)Mean IoU74.2Unverified
10HMNet (ResNet-50)Mean IoU74.1Unverified