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Semantic Segmentation

Papers

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Showing 6651–6700 of 14763 papers

TitleStatusHype
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features—0
Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation—0
Improving Semantic Segmentation via Self-Training—0
AF_2: Adaptive Focus Framework for Aerial Imagery Segmentation—0
Learning Active Contour Models for Medical Image Segmentation—0
Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers—0
ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models—0
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss—0
Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time—0
Learning Affinity via Spatial Propagation Network—0
Improving Spatial Codification in Semantic Segmentation—0
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context—0
Fully Convolutional Deep Network Architectures for Automatic Short Glass Fiber Semantic Segmentation from CT scans—0
Fully Convolutional Adaptation Networks for Semantic Segmentation—0
Fully Connected Deep Structured Networks—0
Fully Complex-valued Fully Convolutional Multi-feature Fusion Network (FC2MFN) for Building Segmentation of InSAR images—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
Fully-Automatic Synapse Prediction and Validation on a Large Data Set—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
Conditioning Covert Geo-Location (CGL) Detection on Semantic Class Information—0
A-Eye: Driving with the Eyes of AI for Corner Case Generation—0
Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder—0
Learning 3D Semantic Segmentation with only 2D Image Supervision—0
Improving Translation Invariance in Convolutional Neural Networks with Peripheral Prediction Padding—0
Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model—0
Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation—0
Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?—0
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy—0
Improving Unsupervised Video Object Segmentation via Fake Flow Generation—0
Data variation-aware medical image segmentation—0
Fully automated quantification of in vivo viscoelasticity of prostate zones using magnetic resonance elastography with Dense U-net segmentation—0
Fully-Automated Packaging Structure Recognition in Logistics Environments—0
Conditional Segmentation in Lieu of Image Registration—0
A-expansion for multiple "hedgehog" shapes—0
Fully Automated Organ Segmentation in Male Pelvic CT Images—0
Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks—0
A Classification Refinement Strategy for Semantic Segmentation—0
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review—0
3D Guided Weakly Supervised Semantic Segmentation—0
Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions—0
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation—0
Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation—0
Incorporating Depth into both CNN and CRF for Indoor Semantic Segmentation—0
Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation—0
Learning a General Model: Folding Clothing with Topological Dynamics—0
Incorporating Human Domain Knowledge in 3D LiDAR-based Semantic Segmentation—0
Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision—0
Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation—0
Fully Aligned Network for Referring Image Segmentation—0
Conditional Random Field and Deep Feature Learning for Hyperspectral Image 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
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