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Semantic Segmentation

Papers

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Showing 11101–11150 of 14763 papers

TitleStatusHype
UAV LiDAR Point Cloud Segmentation of A Stack Interchange with Deep Neural Networks—0
What is Wrong with Continual Learning in Medical Image Segmentation?—0
AutoBSS: An Efficient Algorithm for Block Stacking Style Search—0
Continual Unsupervised Domain Adaptation for Semantic SegmentationCode0
GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation—0
Color Image Segmentation Metrics—0
Multi-Stage Fusion for One-Click Segmentation—0
Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound—0
Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation—0
Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and BeyondCode0
Shape Constrained CNN for Cardiac MR Segmentation with Simultaneous Prediction of Shape and Pose Parameters—0
Localized Interactive Instance Segmentation—0
LID 2020: The Learning from Imperfect Data Challenge Results—0
Learning Panoptic Segmentation from Instance ContoursCode0
Human Perception-based Evaluation Criterion for Ultra-high Resolution Cell Membrane Segmentation—0
Ensembling Low Precision Models for Binary Biomedical Image Segmentation—0
Minimizing Labeling Effort for Tree Skeleton Segmentation using an Automated Iterative Training Methodology—0
CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network—0
Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes—0
Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels—0
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study—0
Encoder-decoder semantic segmentation models for electroluminescence images of thin-film photovoltaic modules—0
A Closed-Loop System for Improving Annotation Quality and Efficiency—0
DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM—0
Semantic Flow-guided Motion Removal Method for Robust Mapping—0
Semantic Segmentation for Partially Occluded Apple Trees Based on Deep Learning—0
PP-LinkNet: Improving Semantic Segmentation of High Resolution Satellite Imagery with Multi-stage Training—0
WeightAlign: Normalizing Activations by Weight Alignment—0
On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation—0
DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen DatasetsCode0
EPYNET: Efficient Pyramidal Network for Clothing Segmentation—0
Bridging 2D and 3D Segmentation Networks for Computation Efficient Volumetric Medical Image Segmentation: An Empirical Study of 2.5D Solutions—0
Land Cover Semantic Segmentation Using ResUNet—0
Automation of Hemocompatibility Analysis Using Image Segmentation and a Random Forest—0
Fully Automatic Wound Segmentation with Deep Convolutional Neural NetworksCode0
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers—0
H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement—0
Hybrid-S2S: Video Object Segmentation with Recurrent Networks and Correspondence MatchingCode0
Contrastive Rendering for Ultrasound Image Segmentation—0
HCNet: Hierarchical Context Network for Semantic Segmentation—0
Selective Information Passing for MR/CT Image SegmentationCode0
Refining Semantic Segmentation with Superpixel by Transparent Initialization and Sparse EncoderCode0
Deep Tiered Image Segmentation For Detecting Internal Ice Layers in Radar Imagery—0
Visual Object Recognition in Indoor Environments Using Topologically Persistent FeaturesCode0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
Parallax Motion Effect Generation Through Instance Segmentation And Depth Estimation—0
Secure 3D medical Imaging—0
Joint COCO and Mapillary Workshop at ICCV 2019: COCO Instance Segmentation Challenge Track—0
Collaboratively boosting data-driven deep learning and knowledge-guided ontological reasoning for semantic segmentation of remote sensing imagery—0
Depth-wise layering of 3d images using dense depth maps: a threshold based approach—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