SOTAVerified

Semantic Segmentation

Papers

Showing 11261150 of 14763 papers

TitleStatusHype
Cached Transformers: Improving Transformers with Differentiable Memory CacheCode1
CAD: Memory Efficient Convolutional Adapter for Segment AnythingCode1
C3S3: Complementary Competition and Contrastive Selection for Semi-Supervised Medical Image SegmentationCode1
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic SegmentationCode1
Detect Any Shadow: Segment Anything for Video Shadow DetectionCode1
Detection and Retrieval of Out-of-Distribution Objects in Semantic SegmentationCode1
D-Former: A U-shaped Dilated Transformer for 3D Medical Image SegmentationCode1
DeRIS: Decoupling Perception and Cognition for Enhanced Referring Image Segmentation through Loopback SynergyCode1
BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in VideoCode1
DermSynth3D: Synthesis of in-the-wild Annotated Dermatology ImagesCode1
Building Extraction from Remote Sensing Images via an Uncertainty-Aware NetworkCode1
BuildingNet: Learning to Label 3D BuildingsCode1
DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image SegmentationCode1
Depth Based Semantic Scene Completion with Position Importance Aware LossCode1
Depth-based 6DoF Object Pose Estimation using Swin TransformerCode1
Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information FusionCode1
BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net modelsCode1
3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance SegmentationCode1
Building a Strong Pre-Training Baseline for Universal 3D Large-Scale PerceptionCode1
Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small DatasetsCode1
Condition-Invariant Semantic SegmentationCode1
Bridging Vision and Language Encoders: Parameter-Efficient Tuning for Referring Image SegmentationCode1
Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic SegmentationCode1
Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation ModelsCode1
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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