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 2326–2350 of 5035 papers

TitleStatusHype
Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning—0
Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code—0
Are foundation models efficient for medical image segmentation?—0
AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation—0
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends—0
3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and Lidar Data—0
Feedback U-net for Cell Image Segmentation—0
Feedback Chain Network For Hippocampus Segmentation—0
CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image Segmentation—0
Feedback Attention for Cell Image Segmentation—0
Are conditional GANs explicitly conditional?—0
FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation—0
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation—0
C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for medical Image Segmentation—0
Clustering using Vector Membership: An Extension of the Fuzzy C-Means Algorithm—0
A Recent Survey of Vision Transformers for Medical Image Segmentation—0
Federated Unsupervised Semantic Segmentation—0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency—0
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation—0
Clustering on the Edge: Learning Structure in Graphs—0
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach—0
Clustering evolving data using kernel-based methods—0
A Random Point Initialization Approach to Image Segmentation with Variational Level-sets—0
A Formalization of Image Vectorization by Region Merging—0
A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks—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