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 791–800 of 1723 papers

TitleStatusHype
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding—0
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation—0
Image Captioning with Integrated Bottom-Up and Multi-level Residual Top-Down Attention for Game Scene Understanding—0
Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks—0
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition—0
IM2CAD—0
Audio-Visual Collaborative Representation Learning for Dynamic Saliency Prediction—0
3D Question Answering for City Scene Understanding—0
A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features—0
MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report—0
Show:102550
← PrevPage 80 of 173Next →
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