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 91–100 of 199 papers

TitleStatusHype
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