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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 199 papers

TitleStatusHype
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects—0
DepthMatch: Semi-Supervised RGB-D Scene Parsing through Depth-Guided Regularization—0
MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation—0
Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing—0
Hardware implementation of timely reliable Bayesian decision-making using memristors—0
OLAF: A Plug-and-Play Framework for Enhanced Multi-object Multi-part Scene Parsing—0
RoadFormer+: Delivering RGB-X Scene Parsing through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion—0
Multi-Grained Contrast for Data-Efficient Unsupervised Representation LearningCode1
PIG: Prompt Images Guidance for Night-Time Scene ParsingCode0
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
Show:102550
← PrevPage 1 of 20Next →
All datasetsPGDP5KCityscapes test

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PGDPNetTotal Accuracy84.7—Unverified
2Inter-GPSTotal Accuracy27.3—Unverified
#ModelMetricClaimedVerifiedStatus
1VCD No CoarsemIoU82.3—Unverified