SOTAVerified

Semantic Segmentation

Papers

Showing 38013825 of 14763 papers

TitleStatusHype
2D Semantic-Guided Semantic Scene CompletionCode1
Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small DatasetsCode1
Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve BackbonesCode1
DeRIS: Decoupling Perception and Cognition for Enhanced Referring Image Segmentation through Loopback SynergyCode1
Anabranch Network for Camouflaged Object SegmentationCode1
DermSynth3D: Synthesis of in-the-wild Annotated Dermatology ImagesCode1
DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image SegmentationCode1
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-RefinementCode1
MudrockNet: Semantic Segmentation of Mudrock SEM Images through Deep LearningCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
Diffusion Features to Bridge Domain Gap for Semantic SegmentationCode1
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality MicroscopyCode1
Diffusion Model as Representation LearnerCode1
Diffusion-based Data Augmentation for Nuclei Image SegmentationCode1
DilateFormer: Multi-Scale Dilated Transformer for Visual RecognitionCode1
Mugs: A Multi-Granular Self-Supervised Learning FrameworkCode1
Multi-Compound Transformer for Accurate Biomedical Image SegmentationCode1
DifFSS: Diffusion Model for Few-Shot Semantic SegmentationCode1
MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based DecodersCode1
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
Medical Transformer: Gated Axial-Attention for Medical Image SegmentationCode1
BPKD: Boundary Privileged Knowledge Distillation For Semantic SegmentationCode1
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic SegmentationCode1
BoxVIS: Video Instance Segmentation with Box AnnotationsCode1
BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance 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
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