SOTAVerified

Scene Parsing

Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Description

Papers

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Showing 76–100 of 199 papers

TitleStatusHype
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images—0
Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene GraphsCode2
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road ScenesCode1
ORDNet: Capturing Omni-Range Dependencies for Scene Parsing—0
An Evolution of CNN Object Classifiers on Low-Resolution Images—0
Interaction via Bi-Directional Graph of Semantic Region Affinity for Scene Parsing—0
Class Attention Network for Semantic Segmentation of Remote Sensing Images—0
Minimal Solvers for Single-View Lens-Distorted Camera Auto-CalibrationCode1
Multi-layer Feature Aggregation for Deep Scene Parsing Models—0
A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide ImagesCode0
LID 2020: The Learning from Imperfect Data Challenge Results—0
Automatic Quantification of Settlement Damage using Deep Learning of Satellite Images—0
GINet: Graph Interaction Network for Scene ParsingCode0
Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene ParsingCode1
Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph GenerationCode1
Non-parametric spatially constrained local prior for scene parsing on real-world data—0
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual RecognitionCode1
Exploiting the Transferability of Deep Learning Systems Across Multi-modal Retinal Scans for Extracting Retinopathy Lesions—0
Variational Context-Deformable ConvNets for Indoor Scene Parsing—0
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local RefinementCode1
Strip Pooling: Rethinking Spatial Pooling for Scene ParsingCode1
EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention FusionCode1
Night-time Scene Parsing with a Large Real Dataset—0
Semantic Flow for Fast and Accurate Scene ParsingCode1
Real-Time Panoptic Segmentation from Dense Detections—0
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All datasetsPGDP5KCityscapes test

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PGDPNetTotal Accuracy84.7—Unverified
2Inter-GPSTotal Accuracy27.3—Unverified
#ModelMetricClaimedVerifiedStatus
1VCD No CoarsemIoU82.3—Unverified