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 91–100 of 199 papers

TitleStatusHype
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
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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