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 201225 of 5035 papers

TitleStatusHype
DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image SegmentationCode2
ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image SegmentationCode2
Deep Incubation: Training Large Models by Divide-and-ConqueringCode2
Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future DirectionsCode2
Densely Connected Parameter-Efficient Tuning for Referring Image SegmentationCode2
Diffusion models as plug-and-play priorsCode2
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense PredictionCode2
Segmentation Loss OdysseyCode2
Seg-metrics: a Python package to compute segmentation metricsCode2
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion ProcessCode2
Customized Segment Anything Model for Medical Image SegmentationCode2
AgileFormer: Spatially Agile Transformer UNet for Medical Image SegmentationCode2
SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation ModelsCode2
ConDSeg: A General Medical Image Segmentation Framework via Contrast-Driven Feature EnhancementCode2
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image SegmentationCode2
Deep Covariance Alignment for Domain Adaptive Remote Sensing Image SegmentationCode2
Contextual Object Detection with Multimodal Large Language ModelsCode2
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image SegmentationCode2
FedFMS: Exploring Federated Foundation Models for Medical Image SegmentationCode2
Rethinking Interactive Image Segmentation with Low Latency High Quality and Diverse PromptsCode2
Tiny and Efficient Model for the Edge Detection GeneralizationCode2
CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge DistillationCode1
Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image SegmentationCode1
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea SegmentationCode1
Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guideCode1
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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