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 126–150 of 199 papers

TitleStatusHype
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects—0
Active Scene Understanding via Online Semantic Reconstruction—0
Adaptive Context Network for Scene Parsing—0
Adaptive Exponential Smoothing for Online Filtering of Pixel Prediction Maps—0
A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing—0
Aerial-PASS: Panoramic Annular Scene Segmentation in Drone Videos—0
Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling—0
An Evolution of CNN Object Classifiers on Low-Resolution Images—0
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors—0
Automatic Quantification of Settlement Damage using Deep Learning of Satellite Images—0
Beyond Forward Shortcuts: Fully Convolutional Master-Slave Networks (MSNets) with Backward Skip Connections for Semantic Segmentation—0
Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation—0
CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing—0
CaseNet: Content-Adaptive Scale Interaction Networks for Scene Parsing—0
CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration—0
Class Attention Network for Semantic Segmentation of Remote Sensing Images—0
Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks—0
Consensus Feature Network for Scene Parsing—0
Context Driven Scene Parsing with Attention to Rare Classes—0
Context-Integrated and Feature-Refined Network for Lightweight Object Parsing—0
Cross-CBAM: A Lightweight network for Scene Segmentation—0
Cutting Edge: Soft Correspondences in Multimodal Scene Parsing—0
Deep and Wide Multiscale Recursive Networks for Robust Image Labeling—0
Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation—0
Deep Deconvolutional Networks for Scene Parsing—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