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 151–199 of 199 papers

TitleStatusHype
MoE-SPNet: A Mixture-of-Experts Scene Parsing Network—0
DeLS-3D: Deep Localization and Segmentation with a 3D Semantic MapCode0
PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing—0
SalientDSO: Bringing Attention to Direct Sparse OdometryCode0
Spatially Constrained Location Prior for Scene Parsing—0
Segmenting Sky Pixels in Images—0
Predicting Scene Parsing and Motion Dynamics in the Future—0
Complete 3D Scene Parsing from an RGBD ImageCode0
Scene Parsing with Global Context EmbeddingCode0
Scale-Adaptive Convolutions for Scene Parsing—0
Soft Correspondences in Multimodal Scene Parsing—0
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions—0
FoveaNet: Perspective-aware Urban Scene Parsing—0
Dual-Glance Model for Deciphering Social RelationshipsCode0
Beyond Forward Shortcuts: Fully Convolutional Master-Slave Networks (MSNets) with Backward Skip Connections for Semantic Segmentation—0
Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRFCode0
Indoor Scene Parsing With Instance Segmentation, Semantic Labeling and Support Relationship Inference—0
Scene Parsing Through ADE20K Dataset—0
Deep Semantics-Aware Photo Adjustment—0
Recurrent Scene Parsing with Perspective Understanding in the LoopCode0
LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks—0
Open Vocabulary Scene Parsing—0
Guaranteed Parameter Estimation for Discrete Energy Minimization—0
Information Pursuit: A Bayesian Framework for Sequential Scene Parsing—0
Video Scene Parsing with Predictive Feature Learning—0
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
Adaptive Exponential Smoothing for Online Filtering of Pixel Prediction Maps—0
Cutting Edge: Soft Correspondences in Multimodal Scene Parsing—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
Context Driven Scene Parsing with Attention to Rare Classes—0
Scene Parsing with Object Instances and Occlusion Ordering—0
Single-View 3D Scene Parsing by Attributed Grammar—0
Deep and Wide Multiscale Recursive Networks for Robust Image Labeling—0
Recurrent Convolutional Neural Networks for Scene Parsing—0
Scene Parsing by Integrating Function, Geometry and Appearance Models—0
Exemplar-Based Face Parsing—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