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 51–75 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
Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense—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
Exploiting the Transferability of Deep Learning Systems Across Multi-modal Retinal Scans for Extracting Retinopathy Lesions—0
DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization—0
Exemplar-Based Face Parsing—0
Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation—0
Aerial-PASS: Panoramic Annular Scene Segmentation in Drone Videos—0
Image Parsing with a Wide Range of Classes and Scene-Level Context—0
Improving Fully Convolution Network for Semantic Segmentation—0
Deep Structured Scene Parsing by Learning with Image Descriptions—0
A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing—0
Deep Semantics-Aware Photo Adjustment—0
Deep Multiphase Level Set for Scene Parsing—0
Deep Hierarchical Parsing for Semantic Segmentation—0
Deep Deconvolutional Networks for Scene Parsing—0
Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation—0
Beyond Forward Shortcuts: Fully Convolutional Master-Slave Networks (MSNets) with Backward Skip Connections for Semantic Segmentation—0
Guaranteed Parameter Estimation for Discrete Energy Minimization—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