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Semantic Segmentation

Papers

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Showing 8301–8325 of 14763 papers

TitleStatusHype
Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting—0
Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation—0
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation—0
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation—0
Syn-Mediverse: A Multimodal Synthetic Dataset for Intelligent Scene Understanding of Healthcare Facilities—0
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis—0
Weakly Supervised Cascaded Convolutional Networks—0
ONeRF: Unsupervised 3D Object Segmentation from Multiple Views—0
SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes—0
Mirror-Yolo: A Novel Attention Focus, Instance Segmentation and Mirror Detection Model—0
One Point, One Object: Simultaneous 3D Object Segmentation and 6-DOF Pose Estimation—0
Generative Synthetic Augmentation using Label-to-Image Translation for Nuclei Image Segmentation—0
Synthetic Convolutional Features for Improved Semantic Segmentation—0
Synthetic data enables faster annotation and robust segmentation for multi-object grasping in clutter—0
Synthetic Data for Deep Learning—0
One Kernel to Solve Nearly Everything: Unified 3D Binary Convolutions for Image Analysis—0
Synthetic Data for Semantic Image Segmentation of Imagery of Unmanned Spacecraft—0
Synthetic Data Generation and Deep Learning for the Topological Analysis of 3D Data—0
Synthetic Data Generation for Bridging Sim2Real Gap in a Production Environment—0
Synthetic Image Data for Deep Learning—0
Synthetic outlier generation for anomaly detection in autonomous driving—0
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation—0
Synthetic-to-real Composite Semantic Segmentation in Additive Manufacturing—0
Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds—0
SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data—0
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All datasetsADE20KNYU-Depth V2Cityscapes testCityscapes valADE20K valPASCAL ContextS3DIS Area5PASCAL VOC 2012 testS3DISScanNetSUN-RGBDDensePASS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-H (M3I Pre-training)Params (M)1,310—Unverified
2ViT-P (InternImage-H)Validation mIoU63.6—Unverified
3ONE-PEACEValidation mIoU63—Unverified
4M3I Pre-training (InternImage-H)Validation mIoU62.9—Unverified
5InternImage-HValidation mIoU62.9—Unverified
6BEiT-3Validation mIoU62.8—Unverified
7EVAValidation mIoU62.3—Unverified
8ViT-P (OneFormer, InternImage-H)Validation mIoU61.6—Unverified
9ViT-Adapter-L (Mask2Former, BEiTv2 pretrain)Validation mIoU61.5—Unverified
10FD-SwinV2-GValidation mIoU61.4—Unverified