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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 168 papers

TitleStatusHype
Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic SegmentationCode1
PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior MaskCode1
Few-Shot Medical Image Segmentation with High-Fidelity PrototypesCode1
Few-Shot Segmentation via Cycle-Consistent TransformerCode1
A Strong Baseline for Generalized Few-Shot Semantic SegmentationCode1
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?Code1
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without AnnotationCode1
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproachCode1
Part-aware Prototype Network for Few-shot Semantic SegmentationCode1
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuningCode1
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight TransformerCode1
PANet: Few-Shot Image Semantic Segmentation with Prototype AlignmentCode1
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot SegmentationCode1
Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural NetworksCode1
Small Object Few-shot Segmentation for Vision-based Industrial InspectionCode1
Unleashing the Potential of the Diffusion Model in Few-shot Semantic SegmentationCode1
On the Texture Bias for Few-Shot CNN SegmentationCode1
Objectness-Aware Few-Shot Semantic SegmentationCode1
Multi-similarity based Hyperrelation Network for few-shot segmentationCode1
Generalized Few-shot Semantic SegmentationCode1
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With SupervoxelsCode1
PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning—0
AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies—0
Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation—0
Analyzing Local Representations of Self-supervised Vision Transformers—0
A New Local Transformation Module for Few-shot Segmentation—0
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation—0
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation—0
APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation—0
Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation—0
Background Clustering Pre-training for Few-shot Segmentation—0
Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenes—0
Boosting Few-Shot Semantic Segmentation Via Segment Anything Model—0
Boosting Few-shot Semantic Segmentation with Transformers—0
[CLS] Token is All You Need for Zero-Shot Semantic Segmentation—0
Dense Affinity Matching for Few-Shot Segmentation—0
Differentiable Meta-learning Model for Few-shot Semantic Segmentation—0
DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining—0
DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation—0
Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation—0
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving—0
Few-shot Segmentation with Optimal Transport Matching and Message Flow—0
Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges—0
Few-Shot Semantic Segmentation Augmented with Image-Level Weak Annotations—0
Few-Shot Semantic Segmentation with Democratic Attention Networks—0
Few-Shot Semantic Segmentation With Cyclic Memory Network—0
Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network—0
FGNet: Towards Filling the Intra-class and Inter-class Gaps for Few-shot Segmentation—0
ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis—0
Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning—0
Show:102550
← PrevPage 2 of 4Next →
All datasetsPASCAL-5i (1-Shot)PASCAL-5i (5-Shot)COCO-20i (1-shot)COCO-20i (5-shot)FSS-1000 (1-shot)FSS-1000 (5-shot)COCO-20i -> Pascal VOC (1-shot)COCO-20i -> Pascal VOC (5-shot)COCO-20i (2-way 1-shot)COCO-20i (10-shot)PASCAL-5i (10-Shot)FSS-1000

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SegGPT (ViT)Mean IoU83.2—Unverified
2DCAMA (ResNet-101)FB-IoU77.6—Unverified
3PGMA-Net (ResNet-101)Mean 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