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 161–170 of 199 papers

TitleStatusHype
Exploiting the Transferability of Deep Learning Systems Across Multi-modal Retinal Scans for Extracting Retinopathy Lesions—0
Exemplar-Based Face Parsing—0
Exploiting Spatial-Temporal Semantic Consistency for Video Scene Parsing—0
Feature boosting with efficient attention for scene parsing—0
Finite State Machines for Semantic Scene Parsing and Segmentation—0
FoveaNet: Perspective-aware Urban Scene Parsing—0
When Segmentation is Not Enough: Rectifying Visual-Volume Discordance Through Multisensor Depth-Refined Semantic Segmentation for Food Intake Tracking in Long-Term Care—0
Fully Decoupled Residual ConvNet for Real-Time Railway Scene Parsing of UAV Aerial Images—0
Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing—0
GEBNet: Graph-Enhancement Branch Network for RGB-T Scene Parsing—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