SOTAVerified

Semantic Segmentation

Papers

Showing 55765600 of 14763 papers

TitleStatusHype
LOD1 3D City Model from LiDAR: The Impact of Segmentation Accuracy on Quality of Urban 3D Modeling and Morphology ExtractionCode0
Log-DenseNet: How to Sparsify a DenseNetCode0
Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised Endoscopic Image SegmentationCode0
LoopDA: Constructing Self-loops to Adapt Nighttime Semantic SegmentationCode0
Local Memory Attention for Fast Video Semantic SegmentationCode0
Localized Super Resolution for Foreground Images using U-Net and MR-CNNCode0
Localizing Infinity-shaped fishes: Sketch-guided object localization in the wildCode0
Local-to-Global Self-Attention in Vision TransformersCode0
Exploiting Temporality for Semi-Supervised Video SegmentationCode0
Localized Adaptive Risk ControlCode0
LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-trainingCode0
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring ExpressionsCode0
Local Context Normalization: Revisiting Local NormalizationCode0
LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel SegmentationCode0
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor SegmentationCode0
Associatively Segmenting Instances and Semantics in Point CloudsCode0
Clearing noisy annotations for computed tomography imagingCode0
Fully-Convolutional Point Networks for Large-Scale Point CloudsCode0
Advancing ALS Applications with Large-Scale Pre-training: Dataset Development and Downstream AssessmentCode0
ClearGrasp: 3D Shape Estimation of Transparent Objects for ManipulationCode0
Location-aware Upsampling for Semantic SegmentationCode0
LoRA-PT: Low-Rank Adapting UNETR for Hippocampus Segmentation Using Principal Tensor Singular Values and VectorsCode0
Exploiting Local Features and Range Images for Small Data Real-Time Point Cloud Semantic SegmentationCode0
Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point RepresentationsCode0
Exploiting Invariance in Training Deep Neural NetworksCode0
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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