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 51–60 of 199 papers

TitleStatusHype
Active Scene Understanding via Online Semantic Reconstruction—0
Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling—0
Finite State Machines for Semantic Scene Parsing and Segmentation—0
Discriminative Map Retrieval Using View-Dependent Map Descriptor—0
Differentiating Features for Scene Segmentation Based on Dedicated Attention Mechanisms—0
CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing—0
ELKPPNet: An Edge-aware Neural Network with Large Kernel Pyramid Pooling for Learning Discriminative Features in Semantic Segmentation—0
Dermoscopic Image Analysis for ISIC Challenge 2018—0
ESCNet: Gaze Target Detection With the Understanding of 3D Scenes—0
DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization—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