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

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Showing 51–100 of 5035 papers

TitleStatusHype
Segment Any Medical Model ExtendedCode3
SegFormer3D: an Efficient Transformer for 3D Medical Image SegmentationCode3
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and MoreCode3
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image SegmentationCode3
ConceptAttention: Diffusion Transformers Learn Highly Interpretable FeaturesCode3
SAM-Med2DCode3
PSALM: Pixelwise SegmentAtion with Large Multi-Modal ModelCode3
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360^Code3
Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoningCode3
MedSegDiff-V2: Diffusion based Medical Image Segmentation with TransformerCode3
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything ModelCode3
SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masksCode3
No time to train! Training-Free Reference-Based Instance SegmentationCode3
RobustSAM: Segment Anything Robustly on Degraded ImagesCode3
Segment Anything without SupervisionCode3
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
ZIM: Zero-Shot Image Matting for AnythingCode3
Breaking reCAPTCHAv2Code3
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360degCode3
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
A Simple Image Segmentation Framework via In-Context ExamplesCode2
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
Masked-attention Mask Transformer for Universal Image SegmentationCode2
ASAM: Boosting Segment Anything Model with Adversarial TuningCode2
DaCapo: a modular deep learning framework for scalable 3D image segmentationCode2
Mask-Adapter: The Devil is in the Masks for Open-Vocabulary SegmentationCode2
MCANet: Medical Image Segmentation with Multi-Scale Cross-Axis AttentionCode2
Medical Vision Generalist: Unifying Medical Imaging Tasks in ContextCode2
M^2SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image SegmentationCode2
LViT: Language meets Vision Transformer in 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
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image SegmentationCode2
LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image SegmentationCode2
LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image SegmentationCode2
IRSAM: Advancing Segment Anything Model for Infrared Small Target DetectionCode2
H-vmunet: High-order Vision Mamba UNet for Medical Image SegmentationCode2
Image Segmentation in Foundation Model Era: A SurveyCode2
Mask2Former for Video Instance SegmentationCode2
MedLSAM: Localize and Segment Anything Model for 3D CT ImagesCode2
Hierarchical Open-vocabulary Universal Image SegmentationCode2
HiDiff: Hybrid Diffusion Framework for Medical Image SegmentationCode2
Generative AI Enables Medical Image Segmentation in Ultra Low-Data RegimesCode2
Generative Medical SegmentationCode2
High-Precision Dichotomous Image Segmentation via Probing Diffusion CapacityCode2
Language-driven Semantic SegmentationCode2
FocalClick: Towards Practical Interactive Image SegmentationCode2
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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