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 101–150 of 199 papers

TitleStatusHype
Unlocking the Full Potential of Small Data with Diverse SupervisionCode0
Differentiating Features for Scene Segmentation Based on Dedicated Attention Mechanisms—0
Adaptive Context Network for Scene Parsing—0
Cooperative Semantic Segmentation and Image Restoration in Adverse Environmental Conditions—0
Segmenting Ships in Satellite Imagery With Squeeze and Excitation U-Net—0
When Segmentation is Not Enough: Rectifying Visual-Volume Discordance Through Multisensor Depth-Refined Semantic Segmentation for Food Intake Tracking in Long-Term Care—0
Deep Multiphase Level Set for Scene Parsing—0
Dynamic Multi-Scale Filters for Semantic SegmentationCode0
What's There in the DarkCode0
What's There in the DarkCode0
Boosting Real-Time Driving Scene Parsing with Shared SemanticsCode0
Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense—0
SPGNet: Semantic Prediction Guidance for Scene Parsing—0
Consensus Feature Network for Scene Parsing—0
Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse ConvolutionsCode0
Context-Integrated and Feature-Refined Network for Lightweight Object Parsing—0
SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV ApplicationsCode0
ELKPPNet: An Edge-aware Neural Network with Large Kernel Pyramid Pooling for Learning Discriminative Features in Semantic Segmentation—0
Active Scene Understanding via Online Semantic Reconstruction—0
Oceanic Eddy Identification Using an AI Scheme—0
Adaptive Pyramid Context Network for Semantic SegmentationCode0
Scene Parsing via Integrated Classification Model and Variance-Based RegularizationCode0
KE-GAN: Knowledge Embedded Generative Adversarial Networks for Semi-Supervised Scene Parsing—0
Structured Knowledge Distillation for Semantic SegmentationCode0
Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing—0
Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More—0
CaseNet: Content-Adaptive Scale Interaction Networks for Scene Parsing—0
GFF: Gated Fully Fusion for Semantic SegmentationCode1
ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed dataCode0
What Synthesis is Missing: Depth Adaptation Integrated with Weak Supervision for Indoor Scene Parsing—0
Structured Knowledge Distillation for Dense PredictionCode0
Toward Achieving Robust Low-Level and High-Level Scene ParsingCode0
Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation—0
Auto-Retoucher(ART) - A framework for Background Replacement and Image EditingCode0
Finite State Machines for Semantic Scene Parsing and Segmentation—0
3D Scene Parsing via Class-Wise Adaptation—0
Context-Aware Synthesis and Placement of Object InstancesCode1
Learning to Exploit Stability for 3D Scene Parsing—0
A Simple Non-i.i.d. Sampling Approach for Efficient Training and Better Generalization—0
Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection—0
Weakly Supervised Scene Parsing with Point-based Distance Metric Learning—0
Synscapes: A Photorealistic Synthetic Dataset for Street Scene ParsingCode0
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction—0
OCNet: Object Context Network for Scene ParsingCode2
PSANet: Point-wise Spatial Attention Network for Scene ParsingCode0
Holistic 3D Scene Parsing and Reconstruction from a Single RGB ImageCode0
Dermoscopic Image Analysis for ISIC Challenge 2018—0
Location Augmentation for CNN—0
MoE-SPNet: A Mixture-of-Experts Scene Parsing Network—0
DeLS-3D: Deep Localization and Segmentation with a 3D Semantic MapCode0
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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