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Semantic Segmentation

Papers

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Showing 14201–14250 of 14763 papers

TitleStatusHype
Freestyle Sketch-in-the-Loop Image Segmentation—0
Free Supervision From Video Games—0
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training—0
Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation—0
Frequency-domain Learning for Volumetric-based 3D Data Perception—0
Frequency Dropout: Feature-Level Regularization via Randomized Filtering—0
FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation—0
FREST: Feature RESToration for Semantic Segmentation under Multiple Adverse Conditions—0
From Active Contours to Minimal Geodesic Paths: New Solutions to Active Contours Problems by Eikonal Equations—0
From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation—0
From Bird's-Eye to Street View: Crafting Diverse and Condition-Aligned Images with Latent Diffusion Model—0
From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation—0
From CNN to Transformer: A Review of Medical Image Segmentation Models—0
From colouring-in to pointillism: revisiting semantic segmentation supervision—0
From Deterministic to Probabilistic: A Novel Perspective on Domain Generalization for Medical Image Segmentation—0
From Explanations to Segmentation: Using Explainable AI for Image Segmentation—0
From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels—0
From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments—0
From Image-level to Pixel-level Labeling with Convolutional Networks—0
From Open-Vocabulary to Vocabulary-Free Semantic Segmentation—0
From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images—0
From Pixels to Images: Deep Learning Advances in Remote Sensing Image Semantic Segmentation—0
From Pixels to Objects: A Hierarchical Approach for Part and Object Segmentation Using Local and Global Aggregation—0
From Semantic Segmentation of Natural Images to Medical Image Segmentation Using ViT-Based Architectures—0
Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards—0
Frustratingly Easy Model Generalization by Dummy Risk Minimization—0
FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels—0
FSNet: A Failure Detection Framework for Semantic Segmentation—0
Full Contextual Attention for Multi-resolution Transformers in Semantic Segmentation—0
Full Point Encoding for Local Feature Aggregation in 3D Point Clouds—0
Fully Aligned Network for Referring Image Segmentation—0
Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks—0
Fully Automated Organ Segmentation in Male Pelvic CT Images—0
Fully-Automated Packaging Structure Recognition in Logistics Environments—0
Fully automated quantification of in vivo viscoelasticity of prostate zones using magnetic resonance elastography with Dense U-net segmentation—0
Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model—0
When Segmentation is Not Enough: Rectifying Visual-Volume Discordance Through Multisensor Depth-Refined Semantic Segmentation for Food Intake Tracking in Long-Term Care—0
Fully-Automatic Synapse Prediction and Validation on a Large Data Set—0
Fully Complex-valued Fully Convolutional Multi-feature Fusion Network (FC2MFN) for Building Segmentation of InSAR images—0
Fully Connected Deep Structured Networks—0
Fully Convolutional Adaptation Networks for Semantic Segmentation—0
Fully Convolutional Deep Network Architectures for Automatic Short Glass Fiber Semantic Segmentation from CT scans—0
Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers—0
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features—0
Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming—0
Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy—0
Fully Convolutional Neural Networks for Crowd Segmentation—0
Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction—0
Fully Hyperbolic Convolutional Neural Networks—0
Fully Self-Supervised Learning for Semantic Segmentation—0
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All datasetsADE20KNYU-Depth V2Cityscapes testCityscapes valADE20K valPASCAL ContextS3DIS Area5PASCAL VOC 2012 testS3DISScanNetSUN-RGBDDensePASS

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
4M3I Pre-training (InternImage-H)Validation mIoU62.9—Unverified
5InternImage-HValidation 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