SOTAVerified

Semantic Segmentation

Papers

Showing 15011525 of 14763 papers

TitleStatusHype
DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable ConvolutionCode1
SegRCDB: Semantic Segmentation via Formula-Driven Supervised LearningCode1
Text-image Alignment for Diffusion-based PerceptionCode1
APNet: Urban-level Scene Segmentation of Aerial Images and Point CloudsCode1
SA2-Net: Scale-aware Attention Network for Microscopic Image SegmentationCode1
Mask4Former: Mask Transformer for 4D Panoptic SegmentationCode1
Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image SegmentationCode1
MoCaE: Mixture of Calibrated Experts Significantly Improves Object DetectionCode1
Treating Motion as Option with Output Selection for Unsupervised Video Object SegmentationCode1
Weakly Supervised Semantic Segmentation by Knowledge Graph InferenceCode1
AsymFormer: Asymmetrical Cross-Modal Representation Learning for Mobile Platform Real-Time RGB-D Semantic SegmentationCode1
3D Indoor Instance Segmentation in an Open-WorldCode1
CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-FreeCode1
Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic SegmentationCode1
Distribution-Aware Continual Test-Time Adaptation for Semantic SegmentationCode1
Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object DatasetCode1
MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for EchocardiographyCode1
Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic SegmentationCode1
PanopticNDT: Efficient and Robust Panoptic MappingCode1
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous DrivingCode1
MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance SegmentationCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
Background Activation Suppression for Weakly Supervised Object Localization and Semantic SegmentationCode1
SAM-OCTA: A Fine-Tuning Strategy for Applying Foundation Model to OCTA Image Segmentation TasksCode1
Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and SamplingCode1
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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