SOTAVerified

Semantic Segmentation

Papers

Showing 58265850 of 14763 papers

TitleStatusHype
Score-Based Generative Models for Medical Image Segmentation using Signed Distance Functions0
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image SegmentationCode1
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN ImagesCode0
Combining visibility analysis and deep learning for refinement of semantic 3D building models by conflict classification0
Iterative Few-shot Semantic Segmentation from Image Label TextCode1
Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass SegmentationCode1
DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation ProblemCode0
Estimating friction coefficient using generative modelling0
Explainable Semantic Medical Image Segmentation with Style0
Dynamic Y-KD: A Hybrid Approach to Continual Instance Segmentation0
Stock Trend Prediction: A Semantic Segmentation Approach0
Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision0
Retinal Image Segmentation with Small Datasets0
MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance SegmentationCode0
Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI ScansCode0
CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image SegmentationCode0
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic SegmentationCode1
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data PruningCode2
FCN+: Global Receptive Convolution Makes FCN Great Again0
DANet: Density Adaptive Convolutional Network with Interactive Attention for 3D Point Clouds0
A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design0
ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR DataCode1
Centroid-centered Modeling for Efficient Vision Transformer Pre-trainingCode0
CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP0
Full Point Encoding for Local Feature Aggregation in 3D Point Clouds0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-H (M3I Pre-training)Params (M)1,310Unverified
2ViT-P (InternImage-H)Validation mIoU63.6Unverified
3ONE-PEACEValidation mIoU63Unverified
4InternImage-HValidation mIoU62.9Unverified
5M3I Pre-training (InternImage-H)Validation mIoU62.9Unverified
6BEiT-3Validation mIoU62.8Unverified
7EVAValidation mIoU62.3Unverified
8ViT-P (OneFormer, InternImage-H)Validation mIoU61.6Unverified
9ViT-Adapter-L (Mask2Former, BEiTv2 pretrain)Validation mIoU61.5Unverified
10FD-SwinV2-GValidation mIoU61.4Unverified