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 176–199 of 199 papers

TitleStatusHype
Improving Fully Convolution Network for Semantic Segmentation—0
Convolutional Neural Network Language ModelsCode0
Multi-Path Feedback Recurrent Neural Network for Scene Parsing—0
Semantic Understanding of Scenes through the ADE20K DatasetCode0
Learning Dynamic Hierarchical Models for Anytime Scene Labeling—0
Scene Parsing with Integration of Parametric and Non-parametric Models—0
Deep Structured Scene Parsing by Learning with Image Descriptions—0
Geometric Scene Parsing with Hierarchical LSTM—0
Cutting Edge: Soft Correspondences in Multimodal Scene Parsing—0
Adaptive Exponential Smoothing for Online Filtering of Pixel Prediction Maps—0
Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering—0
Image Parsing with a Wide Range of Classes and Scene-Level Context—0
Discriminative Map Retrieval Using View-Dependent Map Descriptor—0
Deep Hierarchical Parsing for Semantic Segmentation—0
Large-scale Binary Quadratic Optimization Using Semidefinite Relaxation and Applications—0
Deep Deconvolutional Networks for Scene Parsing—0
Scene Parsing with Object Instances and Occlusion Ordering—0
Single-View 3D Scene Parsing by Attributed Grammar—0
Context Driven Scene Parsing with Attention to Rare Classes—0
Deep and Wide Multiscale Recursive Networks for Robust Image Labeling—0
Recurrent Convolutional Neural Networks for Scene Parsing—0
Exemplar-Based Face Parsing—0
Scene Parsing by Integrating Function, Geometry and Appearance Models—0
Nonparametric Scene Parsing with Adaptive Feature Relevance and Semantic Context—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