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 26012625 of 5035 papers

TitleStatusHype
Task-driven Prompt Evolution for Foundation Models0
Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation0
Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation0
TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation0
TBConvL-Net: A Hybrid Deep Learning Architecture for Robust Medical Image Segmentation0
TBI Image/Text (TBI-IT): Comprehensive Text and Image Datasets for Traumatic Brain Injury Research0
tCURLoRA: Tensor CUR Decomposition Based Low-Rank Parameter Adaptation and Its Application in Medical Image Segmentation0
Technical Note: Feasibility of translating 3.0T-trained Deep-Learning Segmentation Models Out-of-the-Box on Low-Field MRI 0.55T Knee-MRI of Healthy Controls0
TECM: Transfer Learning-based Evidential C-Means Clustering0
Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge0
Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Multi-Scale Aggregation and Anthropic Prior Knowledge0
Template-Based Active Contours0
Temporally Coherent Person Matting Trained on Fake-Motion Dataset0
Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation0
Temporally stable video segmentation without video annotations0
TESA: Tensor Element Self-Attention via Matricization0
Testing the Segment Anything Model on radiology data0
TeST: Test-time Self-Training under Distribution Shift0
Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates0
Test-Time Modality Generalization for Medical Image Segmentation0
Test-Time Training for Deformable Multi-Scale Image Registration0
Text2Layer: Layered Image Generation using Latent Diffusion Model0
Text Augmented Spatial-aware Zero-shot Referring Image Segmentation0
TextDiffSeg: Text-guided Latent Diffusion Model for 3d Medical Images Segmentation0
Text-guided multi-stage cross-perception network for medical image segmentation0
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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