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 21–30 of 199 papers

TitleStatusHype
A Dense Material Segmentation Dataset for Indoor and Outdoor Scene ParsingCode1
BORM: Bayesian Object Relation Model for Indoor Scene RecognitionCode1
AttaNet: Attention-Augmented Network for Fast and Accurate Scene ParsingCode1
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road ScenesCode1
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local RefinementCode1
Global Aggregation then Local Distribution for Scene ParsingCode1
Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene ParsingCode1
Minimal Solvers for Single-View Lens-Distorted Camera Auto-CalibrationCode1
EGFNet: Edge-Aware Guidance Fusion Network for RGB–Thermal Urban Scene ParsingCode1
Fast and Accurate Scene Parsing via Bi-direction Alignment NetworksCode1
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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