SOTAVerified

Semantic Segmentation

Papers

Showing 11011125 of 14763 papers

TitleStatusHype
Data Augmentation-free Unsupervised Learning for 3D Point Cloud UnderstandingCode1
Deep Active Learning for Axon-Myelin Segmentation on Histology DataCode1
D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention MechanismCode1
A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial ImagesCode1
D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in VideosCode1
Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic SegmentationCode1
A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic SegmentationCode1
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based PerceptionCode1
D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in VideosCode1
AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image SegmentationCode1
A context based deep learning approach for unbalanced medical image segmentationCode1
CycleMLP: A MLP-like Architecture for Dense PredictionCode1
CV 3315 Is All You Need : Semantic Segmentation CompetitionCode1
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuningCode1
CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble SupervisionCode1
Cyclic Learning: Bridging Image-level Labels and Nuclei Instance SegmentationCode1
D2Det: Towards High Quality Object Detection and Instance SegmentationCode1
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable FeaturesCode1
A Concise Tiling Strategy for Preserving Spatial Context in Earth Observation ImageryCode1
CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image SegmentationCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
1M parameters are enough? A lightweight CNN-based model for medical image segmentationCode1
Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?Code1
AICSD: Adaptive Inter-Class Similarity Distillation for Semantic SegmentationCode1
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene UnderstandingCode1
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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