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
Improving Fully Convolution Network for Semantic Segmentation—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
Indoor Scene Parsing With Instance Segmentation, Semantic Labeling and Support Relationship Inference—0
Information Pursuit: A Bayesian Framework for Sequential Scene Parsing—0
Deep Structured Scene Parsing by Learning with Image Descriptions—0
1st Place Winner of the 2024 Pixel-level Video Understanding in the Wild (CVPR'24 PVUW) Challenge in Video Panoptic Segmentation and Best Long Video Consistency of Video Semantic Segmentation—0
Deep Semantics-Aware Photo Adjustment—0
Deep Multiphase Level Set for Scene Parsing—0
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions—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
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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