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 1431–1440 of 1723 papers

TitleStatusHype
Relationship Proposal Networks—0
Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration—0
Relevance for Human Robot Collaboration—0
REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision—0
Residual 3D Scene Flow Learning with Context-Aware Feature Extraction—0
Resource-Efficient Multiview Perception: Integrating Semantic Masking with Masked Autoencoders—0
BridgeNet: Comprehensive and Effective Feature Interactions via Bridge Feature for Multi-task Dense Predictions—0
Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets—0
Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection—0
VrR-VG: Refocusing Visually-Relevant Relationships—0
Show:102550
← PrevPage 144 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