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 851–860 of 1723 papers

TitleStatusHype
A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM—0
Making Large Language Models Better Planners with Reasoning-Decision Alignment—0
Manhattan Scene Understanding via XSlit Imaging—0
HyKo: A Spectral Dataset for Scene Understanding—0
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition—0
MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report—0
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors—0
A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding—0
MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation—0
Hybrid Primal Sketch: Combining Analogy, Qualitative Representations, and Computer Vision for 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