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Semantic Segmentation

Papers

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Showing 10251–10300 of 14763 papers

TitleStatusHype
Quality Control in Crowdsourced Object Segmentation—0
Quantification of cardiac capillarization in single-immunostained myocardial slices using weakly supervised instance segmentation—0
Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks—0
Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS—0
Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation—0
Quantity beats quality for semantic segmentation of corrosion in images—0
Quantization of Deep Neural Networks for Accurate Edge Computing—0
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation—0
Quantized Feature Distillation for Network Quantization—0
Quantum Edge Detection for Image Segmentation in Optical Environments—0
Quantum-enhanced unsupervised image segmentation for medical images analysis—0
Quantum Image Segmentation Based on Grayscale Morphology—0
QuantU-Net: Efficient Wearable Medical Imaging Using Bitwidth as a Trainable Parameter—0
Quasi-spectral characterization of intracellular regions in bright-field light microscopy images—0
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge—0
Query Refinement Transformer for 3D Instance Segmentation—0
QueSTMaps: Queryable Semantic Topological Maps for 3D Scene Understanding—0
QuFeX: Quantum feature extraction module for hybrid quantum-classical deep neural networks—0
R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild—0
R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation—0
R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation—0
Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds—0
RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision—0
Radiation Search Operations using Scene Understanding with Autonomous UAV and UGV—0
RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation—0
Radio astronomical images object detection and segmentation: A benchmark on deep learning methods—0
Radiological images and machine learning: trends, perspectives, and prospects—0
Radio U-Net: a convolutional neural network to detect diffuse radio sources in galaxy clusters and beyond—0
Radious: Unveiling the Enigma of Dental Radiology with BEIT Adaptor and Mask2Former in Semantic Segmentation—0
RadSAM: Segmenting 3D radiological images with a 2D promptable model—0
RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Radiology with Zero-Shot Multi-Task Capability—0
RAFT: Robust Augmentation of FeaTures for Image Segmentation—0
RailSem19: A Dataset for Semantic Rail Scene Understanding—0
Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation—0
Rain rendering for evaluating and improving robustness to bad weather—0
Rainy screens: Collecting rainy datasets, indoors—0
RAIS: Robust and Accurate Interactive Segmentation via Continual Learning—0
Random-coupled Neural Network—0
Random Ferns for Semantic Segmentation of PolSAR Images—0
Random Forest with Learned Representations for Semantic Segmentation—0
Randomized 3D Scene Generation for Generalizable Self-Supervised Pre-Training—0
Randomized Dimension Reduction with Statistical Guarantees—0
Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information—0
RangeAugment: Efficient Online Augmentation with Range Learning—0
RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds—0
Ranking pre-trained segmentation models for zero-shot transferability—0
Rapid Deployment of Domain-specific Hyperspectral Image Processors with Application to Autonomous Driving—0
Rapid model transfer for medical image segmentation via iterative human-in-the-loop update: from labelled public to unlabelled clinical datasets for multi-organ segmentation in CT—0
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback—0
FlexiFly: Interfacing the Physical World with Foundation Models Empowered by Reconfigurable Drone Systems—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