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Semantic Segmentation

Papers

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Showing 9826–9850 of 14763 papers

TitleStatusHype
RGPNet: A Real-Time General Purpose Semantic Segmentation—0
RHRSegNet: Relighting High-Resolution Night-Time Semantic Segmentation—0
Using Graph Neural Networks for Program Termination—0
Ridge Regression Neural Network for Pediatric Bone Age Assessment—0
RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models—0
RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation—0
Using Image Priors to Improve Scene Understanding—0
RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images—0
Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation—0
RipVIS: Rip Currents Video Instance Segmentation Benchmark for Beach Monitoring and Safety—0
Extending CLIP's Image-Text Alignment to Referring Image Segmentation—0
RISeg: Robot Interactive Object Segmentation via Body Frame-Invariant Features—0
Risk Assessment for Autonomous Landing in Urban Environments using Semantic Segmentation—0
Risk-Aware Planning by Confidence Estimation using Deep Learning-Based Perception—0
Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS—0
RIT-Eyes: Rendering of near-eye images for eye-tracking applications—0
Using Logic Programming and Kernel-Grouping for Improving Interpretability of Convolutional Neural Networks—0
RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud—0
MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory—0
RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions—0
RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation—0
RMem: Restricted Memory Banks Improve Video Object Segmentation—0
Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks—0
RNGDet++: Road Network Graph Detection by Transformer with Instance Segmentation and Multi-scale Features Enhancement—0
Quantification of cardiac capillarization in single-immunostained myocardial slices using weakly supervised instance segmentation—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