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Semantic Segmentation

Papers

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Showing 10051–10100 of 14763 papers

TitleStatusHype
SAM2Auto: Auto Annotation Using FLASH—0
SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation—0
SAM2 for Image and Video Segmentation: A Comprehensive Survey—0
SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation—0
Prototypical Matching and Open Set Rejection for Zero-Shot Semantic Segmentation—0
SAM2Object: Consolidating View Consistency via SAM2 for Zero-Shot 3D Instance Segmentation—0
Prototypical Kernel Learning and Open-set Foreground Perception for Generalized Few-shot Semantic Segmentation—0
Prototype Refinement Network for Few-Shot Segmentation—0
MP-PolarMask: A Faster and Finer Instance Segmentation for Concave Images—0
Prototype Guided Network for Anomaly Segmentation—0
Prototype Guided Federated Learning of Visual Feature Representations—0
Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentation—0
Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation—0
Prototype-Based Approach for One-Shot Segmentation of Brain Tumors using Few-Shot Learning—0
PROTOTYPE-ASSISTED ADVERSARIAL LEARNING FOR UNSUPERVISED DOMAIN ADAPTATION—0
ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation—0
ProtoSeg: A Prototype-Based Point Cloud Instance Segmentation Method—0
MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression—0
SAM-based instance segmentation models for the automation of structural damage detection—0
ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation—0
SAMBERT: Improve Aspect Sentiment Triplet Extraction by Segmenting the Attention Maps of BERT—0
Wound Segmentation with Dynamic Illumination Correction and Dual-view Semantic Fusion—0
SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding—0
SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation—0
ProtoGMM: Multi-prototype Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation—0
Protecting Semantic Segmentation Models by Using Block-wise Image Encryption with Secret Key from Unauthorized Access—0
Prostate Segmentation using 2D Bridged U-net—0
SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT—0
SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation—0
Prostate Cancer Semantic Segmentation by Gleason Score Group in bi-parametric MRI with Self Attention Model on the Peripheral Zone—0
ProSAM: Enhancing the Robustness of SAM-based Visual Reference Segmentation with Probabilistic Prompts—0
SAM for Poultry Science—0
SAM-guided Graph Cut for 3D Instance Segmentation—0
SAM-Guided Masked Token Prediction for 3D Scene Understanding—0
SAM-guided Pseudo Label Enhancement for Multi-modal 3D Semantic Segmentation—0
SAM-Guided Robust Representation Learning for One-Shot 3D Medical Image Segmentation—0
Learning to Adapt SAM for Segmenting Cross-domain Point Clouds—0
PAM: A Propagation-Based Model for Segmenting Any 3D Objects across Multi-Modal Medical Images—0
Variance-insensitive and Target-preserving Mask Refinement for Interactive Image Segmentation—0
SAMIC: Segment Anything with In-Context Spatial Prompt Engineering—0
SAM-IF: Leveraging SAM for Incremental Few-Shot Instance Segmentation—0
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks—0
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model—0
Proposal-free Network for Instance-level Object Segmentation—0
Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity—0
SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation—0
SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective—0
SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation—0
SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting—0
Propagating Semantic Labels in Video Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-H (M3I Pre-training)Params (M)1,310—Unverified
2ViT-P (InternImage-H)Validation mIoU63.6—Unverified
3ONE-PEACEValidation mIoU63—Unverified
4InternImage-HValidation mIoU62.9—Unverified
5M3I Pre-training (InternImage-H)Validation mIoU62.9—Unverified
6BEiT-3Validation mIoU62.8—Unverified
7EVAValidation mIoU62.3—Unverified
8ViT-P (OneFormer, InternImage-H)Validation mIoU61.6—Unverified
9ViT-Adapter-L (Mask2Former, BEiTv2 pretrain)Validation mIoU61.5—Unverified
10FD-SwinV2-GValidation mIoU61.4—Unverified