SOTAVerified

Semantic Segmentation

Papers

Showing 70017025 of 14763 papers

TitleStatusHype
Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic SegmentationCode1
Extending nnU-Net is all you need0
Deep Structural Causal Shape Models0
Consistency Regularization for Domain AdaptationCode0
CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical ImageCode0
Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image SegmentationCode0
InstanceFormer: An Online Video Instance Segmentation FrameworkCode1
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation0
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed LearningCode0
Prompt-Matched Semantic Segmentation0
Multi-Granularity Distillation Scheme Towards Lightweight Semi-Supervised Semantic SegmentationCode1
SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationCode1
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
GCISG: Guided Causal Invariant Learning for Improved Syn-to-real Generalization0
Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic SegmentationCode1
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic SegmentationCode2
Persuasion Strategies in AdvertisementsCode1
Net2Brain: A Toolbox to compare artificial vision models with human brain responsesCode1
Test-time Training for Data-efficient UCDRCode0
PyMIC: A deep learning toolkit for annotation-efficient medical image segmentationCode2
EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation0
Low-light Enhancement Method Based on Attention Map Net0
Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical StructuresCode1
Unifying Visual Perception by Dispersible Points LearningCode1
Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss0
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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