SOTAVerified

Semantic Segmentation

Papers

Showing 97019725 of 14763 papers

TitleStatusHype
Hierarchical Image Peeling: A Flexible Scale-space Filtering FrameworkCode1
Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient ImagesCode0
DARCNN: Domain Adaptive Region-based Convolutional Neural Network forUnsupervised Instance Segmentation in Biomedical ImagesCode0
DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical ImagesCode0
Recursively Refined R-CNN: Instance Segmentation with Self-RoI RebalancingCode0
Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy0
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision0
Decomposing 3D Scenes into Objects via Unsupervised Volume SegmentationCode1
LiftPool: Bidirectional ConvNet Pooling0
Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic SegmentationCode1
Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation0
Half-Real Half-Fake Distillation for Class-Incremental Semantic Segmentation0
A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite ImageryCode1
Learning to Track Instances without Video Annotations0
Anytime Dense Prediction with Confidence AdaptivityCode1
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking0
Learning Foreground-Background Segmentation from Improved Layered GANs0
The surprising impact of mask-head architecture on novel class segmentationCode0
Linear Semantics in Generative Adversarial NetworksCode1
FAPIS: A Few-shot Anchor-free Part-based Instance SegmenterCode1
Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray Images0
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain AdaptationCode1
Scale-aware Automatic Augmentation for Object DetectionCode1
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity PerspectiveCode1
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation0
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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