SOTAVerified

Segmentation

Papers

Showing 126150 of 13072 papers

TitleStatusHype
A large annotated medical image dataset for the development and evaluation of segmentation algorithmsCode2
FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anythingCode2
Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-trainCode2
FOCUS: Towards Universal Foreground SegmentationCode2
FreeSOLO: Learning to Segment Objects without AnnotationsCode2
AgileFormer: Spatially Agile Transformer UNet for Medical Image SegmentationCode2
FocalClick: Towards Practical Interactive Image SegmentationCode2
Focal Modulation NetworksCode2
FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor SynthesisCode2
Find Any Part in 3DCode2
A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT imagesCode2
Find First, Track Next: Decoupling Identification and Propagation in Referring Video Object SegmentationCode2
FedFMS: Exploring Federated Foundation Models for Medical Image SegmentationCode2
FEC: Fast Euclidean Clustering for Point Cloud SegmentationCode2
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary SegmentationCode2
F-LMM: Grounding Frozen Large Multimodal ModelsCode2
FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRICode2
Fast Online Object Tracking and Segmentation: A Unifying ApproachCode2
A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ SegmentationCode2
FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image SegmentationCode2
FastInst: A Simple Query-Based Model for Real-Time Instance SegmentationCode2
FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation ModelsCode2
Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in Land Cover Mapping via Hybrid Semantic Segmentation FrameworkCode2
EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image SegmentationCode2
ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image SegmentationCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1unSAM+ (Semi-supervised)Average Precision42.8Unverified
2SAMAverage Precision38.9Unverified
#ModelMetricClaimedVerifiedStatus
1HNN10%20Unverified
#ModelMetricClaimedVerifiedStatus
1ABANetF1 Score96.82Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50 + DeepLabV3+F1 score0.77Unverified
#ModelMetricClaimedVerifiedStatus
1LangGasIoU0.69Unverified