SOTAVerified

Scene Understanding

Scene understanding involves interpreting the visual information of a scene, including objects, their spatial relationships, and the overall layout. It goes beyond simple object recognition by considering the context and how objects relate to each other and the environment.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–775 of 1723 papers

TitleStatusHype
Improving Human-Object Interaction Detection via Phrase Learning and Label Composition—0
Improving Building Segmentation for Off-Nadir Satellite Imagery—0
Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds—0
Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation—0
Improving 6D Object Pose Estimation of metallic Household and Industry Objects—0
Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation—0
LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding—0
A Multiple-View Geometric Model for Specularity Prediction on General Curved Surfaces—0
Looking Beyond the Visible Scene—0
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement—0
Image-to-Height Domain Translation for Synthetic Aperture Sonar—0
Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery—0
Language-EXtended Indoor SLAM (LEXIS): A Versatile System for Real-time Visual Scene Understanding—0
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving—0
Large Language Models for Autonomous Driving (LLM4AD): Concept, Benchmark, Experiments, and Challenges—0
Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm—0
Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems—0
A Comprehensive Review of Modern Object Segmentation Approaches—0
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning—0
Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment—0
Image Parsing with Stochastic Scene Grammar—0
Learning 3D Robotics Perception using Inductive Priors—0
Deep Contextual Attention for Human-Object Interaction Detection—0
Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions—0
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding—0
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All datasetsSemantic Scene Understanding Challenge (passive actuation & ground-truth localisation)ADE20K valSemantic Scene Understanding Challenge (active actuation & ground-truth localisation)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.44—Unverified
2Team VGAI (TCS Research)OMQ0.37—Unverified
3Demo_semantic_SLAMOMQ0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1CPN(ResNet-101)Mean IoU46.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.35—Unverified