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Segmentation

Papers

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Showing 6926–6950 of 13072 papers

TitleStatusHype
Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation—0
FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images—0
L-FUSION: Laplacian Fetal Ultrasound Segmentation & Uncertainty Estimation—0
Classroom Slide Narration System—0
A recurrent connectionist model of melody perception : An exploration using TRACX2—0
LIBSVX: A Supervoxel Library and Benchmark for Early Video Processing—0
LiCAR: pseudo-RGB LiDAR image for CAR segmentation—0
Class-level Multiple Distributions Representation are Necessary for Semantic Segmentation—0
Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation—0
MEDOE: A Multi-Expert Decoder and Output Ensemble Framework for Long-tailed Semantic Segmentation—0
LiDAR Based Semantic Perception for Forklifts in Outdoor Environments—0
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving—0
LiDAR-Camera Fusion for Video Panoptic Segmentation without Video Training—0
MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM—0
LiDARFormer: A Unified Transformer-based Multi-task Network for LiDAR Perception—0
Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation—0
LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception—0
LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network—0
LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation—0
MEGA: Multimodal Alignment Aggregation and Distillation For Cinematic Video Segmentation—0
LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation—0
A Cascade Model for Proposition Extraction in Argumentation—0
A Recent Survey of Vision Transformers for Medical Image Segmentation—0
Lifting GIS Maps into Strong Geometric Context for Scene Understanding—0
FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images—0
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All datasetsSA-1B.MFSDMMFloodSimGas

Benchmark Results

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