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Semantic Segmentation

Papers

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Showing 5801–5850 of 14763 papers

TitleStatusHype
Geometry Aware Field-to-field Transformations for 3D Semantic Segmentation—0
Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards—0
Improving Bird's Eye View Semantic Segmentation by Task Decomposition—0
Geometry-Aware Network for Domain Adaptive Semantic Segmentation—0
Geometry-Aware Recurrent Neural Networks for Active Visual Recognition—0
Geometry-Aware Segmentation of Remote Sensing Images via Implicit Height Estimation—0
GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes—0
Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery—0
GeoF: Geodesic Forests for Learning Coupled Predictors—0
Geomorphological Analysis Using Unpiloted Aircraft Systems, Structure from Motion, and Deep Learning—0
Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation—0
Geodesic Paths for Image Segmentation with Implicit Region-based Homogeneity Enhancement—0
Geodesic Models with Convexity Shape Prior—0
Contextual Graph Reasoning Networks—0
A tailored Handwritten-Text-Recognition System for Medieval Latin—0
A Formalization of Image Vectorization by Region Merging—0
A Clustering Method Based on Information Entropy Payload—0
Gen-n-Val: Agentic Image Data Generation and Validation—0
GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR—0
A System for Accurate Tracking and Video Recordings of Rodent Eye Movements using Convolutional Neural Networks for Biomedical Image Segmentation—0
GH-Feat: Learning Versatile Generative Hierarchical Features from GANs—0
GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts—0
A Formal Evaluation of PSNR as Quality Measurement Parameter for Image Segmentation Algorithms—0
Context Tricks for Cheap Semantic Segmentation—0
Generic Perceptual Loss for Modeling Structured Output Dependencies—0
GIMP and Wavelets for Medical Image Processing: Enhancing Images of the Fundus of the Eye—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
GIN: Generative INvariant Shape Prior for Amodal Instance Segmentation—0
A System-driven Automatic Ground Truth Generation Method for DL Inner-City Driving Corridor Detectors—0
Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans—0
Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism—0
Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans—0
Improved Workflow for Unsupervised Multiphase Image Segmentation—0
Gland Instance Segmentation by Deep Multichannel Side Supervision—0
ContextSeg: Sketch Semantic Segmentation by Querying the Context with Attention—0
Gland Instance Segmentation Using Deep Multichannel Neural Networks—0
Glass Segmentation Using Intensity and Spectral Polarization Cues—0
Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer—0
A Systematic Approach for MRI Brain Tumor Localization, and Segmentation using Deep Learning and Active Contouring—0
GLH-Water: A Large-Scale Dataset for Global Surface Water Detection in Large-Size Very-High-Resolution Satellite Imagery—0
GLID: Pre-training a Generalist Encoder-Decoder Vision Model—0
Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections—0
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation—0
Context-Reinforced Semantic Segmentation—0
Improved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames—0
Contextrast: Contextual Contrastive Learning for Semantic Segmentation—0
Continuous Online Adaptation Driven by User Interaction for Medical Image Segmentation—0
Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation—0
Global Attention-Guided Dual-Domain Point Cloud Feature Learning for Classification and Segmentation—0
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data—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