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 131–140 of 199 papers

TitleStatusHype
Aerial-PASS: Panoramic Annular Scene Segmentation in Drone Videos—0
Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling—0
An Evolution of CNN Object Classifiers on Low-Resolution Images—0
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors—0
Automatic Quantification of Settlement Damage using Deep Learning of Satellite Images—0
Beyond Forward Shortcuts: Fully Convolutional Master-Slave Networks (MSNets) with Backward Skip Connections for Semantic Segmentation—0
Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation—0
CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing—0
CaseNet: Content-Adaptive Scale Interaction Networks for Scene Parsing—0
CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration—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