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 3526–3550 of 5035 papers

TitleStatusHype
C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for medical Image Segmentation—0
CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image Segmentation—0
CNN-aware Binary Map for General Semantic Segmentation—0
CNN-Based Deep Architecture for Reinforced Concrete Delamination Segmentation Through Thermography—0
CNN-based Semantic Segmentation using Level Set Loss—0
CNN in CT Image Segmentation: Beyound Loss Function for Expoliting Ground Truth Images—0
CNN-Transformer Rectified Collaborative Learning for Medical Image Segmentation—0
CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation—0
CoFiNet: Unveiling Camouflaged Objects with Multi-Scale Finesse—0
Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction—0
Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation—0
Collaborative Position Reasoning Network for Referring Image Segmentation—0
Collective Robustness Certificates—0
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks—0
Color image segmentation based on a convex K-means approach—0
Color Image Segmentation Metrics—0
Color Image Segmentation using Adaptive Particle Swarm Optimization and Fuzzy C-means—0
Color Image Segmentation Using Multi-Objective Swarm Optimizer and Multi-level Histogram Thresholding—0
Color Recognition in Challenging Lighting Environments: CNN Approach—0
Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation—0
Combinatorial clustering and the beta negative binomial process—0
Combinatorial Energy Learning for Image Segmentation—0
Combined Approach for Image Segmentation—0
Combiner and HyperCombiner Networks: Rules to Combine Multimodality MR Images for Prostate Cancer Localisation—0
Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation—0
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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