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

Showing 2650 of 5035 papers

TitleStatusHype
Semantic-SAM: Segment and Recognize Anything at Any GranularityCode4
Attention on the SphereCode4
LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and EditingCode4
Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image SegmentationCode4
VM-UNet: Vision Mamba UNet for Medical Image SegmentationCode4
What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?Code3
Segment Any Medical Model ExtendedCode3
SAM-Med2DCode3
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and MoreCode3
SegFormer3D: an Efficient Transformer for 3D Medical Image SegmentationCode3
SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image SegmentationCode3
RobustSAM: Segment Anything Robustly on Degraded ImagesCode3
SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masksCode3
Segment Anything without SupervisionCode3
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360degCode3
MedSegDiff-V2: Diffusion based Medical Image Segmentation with TransformerCode3
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
No time to train! Training-Free Reference-Based Instance SegmentationCode3
Point-SAM: Promptable 3D Segmentation Model for Point CloudsCode3
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360^Code3
ConceptAttention: Diffusion Transformers Learn Highly Interpretable FeaturesCode3
Breaking reCAPTCHAv2Code3
MA-Net: A Multi-Scale Attention Network for Liver and Tumor SegmentationCode3
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image SegmentationCode3
PSALM: Pixelwise SegmentAtion with Large Multi-Modal ModelCode3
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Benchmark Results

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