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 1691–1700 of 1723 papers

TitleStatusHype
Improving Building Segmentation for Off-Nadir Satellite Imagery—0
Improving Human-Object Interaction Detection via Phrase Learning and Label Composition—0
A Dynamic Feature Interaction Framework for Multi-task Visual Perception—0
Improving Online Lane Graph Extraction by Object-Lane Clustering—0
Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks—0
InCoRo: In-Context Learning for Robotics Control with Feedback Loops—0
Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles—0
TransforMerger: Transformer-based Voice-Gesture Fusion for Robust Human-Robot Communication—0
Incorporating Scene Context and Object Layout into Appearance Modeling—0
Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs—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