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 101125 of 168 papers

TitleStatusHype
Unsupervised Deep Learning for Bayesian Brain MRI SegmentationCode0
Attentional Prototype Inference for Few-Shot SegmentationCode0
Few-shot semantic segmentation via mask aggregationCode0
Self-Regularized Prototypical Network for Few-Shot Semantic Segmentation0
AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies0
Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation0
Analyzing Local Representations of Self-supervised Vision Transformers0
A New Local Transformation Module for Few-shot Segmentation0
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation0
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation0
APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation0
Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation0
Background Clustering Pre-training for Few-shot Segmentation0
Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenes0
Boosting Few-Shot Semantic Segmentation Via Segment Anything Model0
Boosting Few-shot Semantic Segmentation with Transformers0
[CLS] Token is All You Need for Zero-Shot Semantic Segmentation0
Dense Affinity Matching for Few-Shot Segmentation0
Differentiable Meta-learning Model for Few-shot Semantic Segmentation0
DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining0
DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation0
Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation0
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving0
Few-shot Segmentation with Optimal Transport Matching and Message Flow0
Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges0
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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