SOTAVerified

Semantic Segmentation

Papers

Showing 17011725 of 14763 papers

TitleStatusHype
Towards accurate instance segmentation in large-scale LiDAR point cloudsCode1
Conditional Boundary Loss for Semantic SegmentationCode1
MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation DatasetsCode1
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CTCode1
DifFSS: Diffusion Model for Few-Shot Semantic SegmentationCode1
SAMAug: Point Prompt Augmentation for Segment Anything ModelCode1
RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object SegmentationCode1
Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain GeneralizationCode1
SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D Object Pose EstimationCode1
MobileViG: Graph-Based Sparse Attention for Mobile Vision ApplicationsCode1
Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic SegmentationCode1
GraSS: Contrastive Learning with Gradient Guided Sampling Strategy for Remote Sensing Image Semantic SegmentationCode1
1M parameters are enough? A lightweight CNN-based model for medical image segmentationCode1
SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar SegmentationCode1
High-Quality Unknown Object Instance Segmentation via Quadruple Boundary Error RefinementCode1
Land Cover Segmentation with Sparse Annotations from Sentinel-2 ImageryCode1
What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic SegmentationCode1
Delving into Crispness: Guided Label Refinement for Crisp Edge DetectionCode1
PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and AggregationCode1
MIMIC: Masked Image Modeling with Image CorrespondencesCode1
SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV FusionCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
When SAM Meets Sonar ImagesCode1
How to Efficiently Adapt Large Segmentation Model(SAM) to Medical ImagesCode1
Segmentation and Tracking of Vegetable Plants by Exploiting Vegetable Shape Feature for Precision Spray of Agricultural RobotsCode1
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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