SOTAVerified

Semantic Segmentation

Papers

Showing 13011325 of 14763 papers

TitleStatusHype
Decomposed Knowledge Distillation for Class-Incremental Semantic SegmentationCode1
AerialFormer: Multi-resolution Transformer for Aerial Image SegmentationCode1
Decoder Denoising Pretraining for Semantic SegmentationCode1
Decomposing 3D Scenes into Objects via Unsupervised Volume SegmentationCode1
DDANet: Dual Decoder Attention Network for Automatic Polyp SegmentationCode1
DEAL: Difficulty-aware Active Learning for Semantic SegmentationCode1
DCT-Mask: Discrete Cosine Transform Mask Representation for Instance SegmentationCode1
DCSEG: Decoupled 3D Open-Set Segmentation using Gaussian SplattingCode1
DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images SegmentationCode1
DeBiFormer: Vision Transformer with Deformable Agent Bi-level Routing AttentionCode1
Decoupled Dynamic Filter NetworksCode1
A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed SupervisionCode1
DB-SAM: Delving into High Quality Universal Medical Image SegmentationCode1
DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image SegmentationCode1
A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic SegmentationCode1
DC-SAM: In-Context Segment Anything in Images and Videos via Dual ConsistencyCode1
Dataset Enhancement with Instance-Level AugmentationsCode1
DatasetGAN: Efficient Labeled Data Factory with Minimal Human EffortCode1
DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image SegmentationCode1
Decoupled Local Aggregation for Point Cloud LearningCode1
DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real EnvironmentsCode1
Unified Domain Adaptive Semantic SegmentationCode1
DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic SegmentationCode1
Data Augmentation-free Unsupervised Learning for 3D Point Cloud UnderstandingCode1
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive 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