SOTAVerified

Semantic Segmentation

Papers

Showing 39263950 of 14763 papers

TitleStatusHype
MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation DetectionCode1
MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under UncertaintyCode1
Learning Spatio-Appearance Memory Network for High-Performance Visual TrackingCode1
AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image SegmentationCode1
MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation towards Unannotated ModalitiesCode1
MUXConv: Information Multiplexing in Convolutional Neural NetworksCode1
NamedMask: Distilling Segmenters from Complementary Foundation ModelsCode1
Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor SceneCode1
An End-to-End Computer Vision Methodology for Quantitative MetallographyCode1
Diffusion-based Data Augmentation for Nuclei Image SegmentationCode1
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image SegmentationCode1
MSLAU-Net: A Hybird CNN-Transformer Network for Medical Image SegmentationCode1
Bounding Box Tightness Prior for Weakly Supervised Image SegmentationCode1
AnatoMask: Enhancing Medical Image Segmentation with Reconstruction-guided Self-maskingCode1
Diffusion Features to Bridge Domain Gap for Semantic SegmentationCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
Diffusion Model as Representation LearnerCode1
Discovering Object Masks with Transformers for Unsupervised Semantic SegmentationCode1
Multi-view Inverse Rendering for Large-scale Real-world Indoor ScenesCode1
Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image SegmentationCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
Multi-View Radar Semantic SegmentationCode1
BlendMask: Top-Down Meets Bottom-Up for Instance SegmentationCode1
DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic SegmentationCode1
Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic SegmentationCode1
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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
4M3I Pre-training (InternImage-H)Validation mIoU62.9Unverified
5InternImage-HValidation 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