SOTAVerified

Semantic Segmentation

Papers

Showing 876900 of 14763 papers

TitleStatusHype
Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation0
Disentangling CLIP for Multi-Object Perception0
ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation ModelsCode0
DILLEMA: Diffusion and Large Language Models for Multi-Modal AugmentationCode0
Rethinking Vision Transformer for Object Centric Foundation ModelsCode0
RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 20
Muographic Image Upsampling with Machine Learning for Built Infrastructure Applications0
Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation0
A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation0
Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation0
Memory Efficient Transformer Adapter for Dense Predictions0
Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and ClassificationCode1
UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic SegmentationCode1
IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning0
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings0
UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image segmentationCode1
Test Time Training for 4D Medical Image InterpolationCode0
FSPGD: Rethinking Black-box Attacks on Semantic SegmentationCode1
Geometric Framework for Cell Oversegmentation0
Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation ModelsCode0
SAM-guided Pseudo Label Enhancement for Multi-modal 3D Semantic Segmentation0
A Turing Test for Artificial Nets devoted to model Human Vision0
Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation0
Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic PerspectiveCode0
CAD: Confidence-Aware Adaptive Displacement for Semi-Supervised Medical Image Segmentation0
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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