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 81–90 of 199 papers

TitleStatusHype
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors—0
ELKPPNet: An Edge-aware Neural Network with Large Kernel Pyramid Pooling for Learning Discriminative Features in Semantic Segmentation—0
Class Attention Network for Semantic Segmentation of Remote Sensing Images—0
CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration—0
An Evolution of CNN Object Classifiers on Low-Resolution Images—0
Adaptive Context Network for Scene Parsing—0
CaseNet: Content-Adaptive Scale Interaction Networks for Scene Parsing—0
Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling—0
Discriminative Map Retrieval Using View-Dependent Map Descriptor—0
Differentiating Features for Scene Segmentation Based on Dedicated Attention Mechanisms—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