SOTAVerified

Image Segmentation

Image Segmentation is a computer vision task that involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of an object. The goal of image segmentation is to assign a unique label or category to each pixel in the image, so that pixels with similar attributes are grouped together.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 5035 papers

TitleStatusHype
UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image SegmentationCode3
Interactive Medical Image Segmentation: A Benchmark Dataset and BaselineCode3
Segment Any Medical Model ExtendedCode3
SegFormer3D: an Efficient Transformer for 3D Medical Image SegmentationCode3
SAM-Med2DCode3
SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image SegmentationCode3
SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masksCode3
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and MoreCode3
PSALM: Pixelwise SegmentAtion with Large Multi-Modal ModelCode3
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360degCode3
Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoningCode3
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?Code3
MedSegDiff-V2: Diffusion based Medical Image Segmentation with TransformerCode3
No time to train! Training-Free Reference-Based Instance SegmentationCode3
RobustSAM: Segment Anything Robustly on Degraded ImagesCode3
Segment Anything without SupervisionCode3
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360^Code3
Point-SAM: Promptable 3D Segmentation Model for Point CloudsCode3
Medical Image Segmentation Review: The success of U-NetCode2
MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image SegmentationCode2
Medical Image Segmentation with Domain Adaptation: A SurveyCode2
MCANet: Medical Image Segmentation with Multi-Scale Cross-Axis AttentionCode2
3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentationCode2
MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image SegmentationCode2
Mask-Adapter: The Devil is in the Masks for Open-Vocabulary SegmentationCode2
AgileFormer: Spatially Agile Transformer UNet for Medical Image SegmentationCode2
DaCapo: a modular deep learning framework for scalable 3D image segmentationCode2
A Simple Image Segmentation Framework via In-Context ExamplesCode2
Masked-attention Mask Transformer for Universal Image SegmentationCode2
Medical Vision Generalist: Unifying Medical Imaging Tasks in ContextCode2
M^2SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image SegmentationCode2
Are Vision xLSTM Embedded UNet More Reliable in Medical 3D Image Segmentation?Code2
LLM-Seg: Bridging Image Segmentation and Large Language Model ReasoningCode2
ASAM: Boosting Segment Anything Model with Adversarial TuningCode2
LViT: Language meets Vision Transformer in Medical Image SegmentationCode2
LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image SegmentationCode2
LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image SegmentationCode2
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image SegmentationCode2
IRSAM: Advancing Segment Anything Model for Infrared Small Target DetectionCode2
Image Segmentation in Foundation Model Era: A SurveyCode2
H-vmunet: High-order Vision Mamba UNet for Medical Image SegmentationCode2
Language-driven Semantic SegmentationCode2
Mask2Former for Video Instance SegmentationCode2
MedLSAM: Localize and Segment Anything Model for 3D CT ImagesCode2
Hierarchical Open-vocabulary Universal Image SegmentationCode2
High-Precision Dichotomous Image Segmentation via Probing Diffusion CapacityCode2
Generative Medical SegmentationCode2
Generative AI Enables Medical Image Segmentation in Ultra Low-Data RegimesCode2
HiDiff: Hybrid Diffusion Framework for Medical Image SegmentationCode2
Show:102550
← PrevPage 2 of 101Next →
All datasetsMSD (Mirror Segmentation Dataset)PMDMAS3KPascal Panoptic PartsRMASPASCAL VOCHuTu 80MARIDACOCO val2017ImageNetMSD HeartOxfordPets

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SAM2-UNetIoU0.92—Unverified
2HetNetIoU0.83—Unverified
3PMDIoU0.82—Unverified
4SANetIoU0.8—Unverified
5MirrorNetIoU0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1SAM2-UNetIoU0.73—Unverified
2HetNetIoU0.69—Unverified
3SANetIoU0.67—Unverified
4PMDIoU0.66—Unverified
5MirrorNetIoU0.59—Unverified
#ModelMetricClaimedVerifiedStatus
1SAM2-UNetmIoU0.8—Unverified
2MAS-SAMmIoU0.79—Unverified
3MASNetmIoU0.74—Unverified
4ZoomNetmIoU0.74—Unverified
#ModelMetricClaimedVerifiedStatus
1HIPIE (ViT-H)mIoUPartS63.8—Unverified
2PPSmIoUPartS58.6—Unverified
3HIPIE (ResNet-50)mIoUPartS57.2—Unverified
4JPPFmIoUPartS54.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MAS-SAMmIoU0.74—Unverified
2SAM2-UNetmIoU0.74—Unverified
3MASNetmIoU0.73—Unverified
4ZoomNetmIoU0.73—Unverified
#ModelMetricClaimedVerifiedStatus
1OneNete,4-CmIoU63.6—Unverified
2OneNete,4-SmAP0.552.75—Unverified
3OneNeted,4mIoU14.9—Unverified
#ModelMetricClaimedVerifiedStatus
1UNetRDice0.98—Unverified
2PALEDDice0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1ResAttUNetIoU0.67—Unverified
2UNetIoU0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1SynCo (ResNet-50) 200epmask AP35.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MobileOne-S0GFLOPs0.28—Unverified
#ModelMetricClaimedVerifiedStatus
1OneNete,4mIoU6.6—Unverified
#ModelMetricClaimedVerifiedStatus
1OneNete,4-CDice Score0.97—Unverified