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

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Showing 901–910 of 1723 papers

TitleStatusHype
Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models—0
Do Deep Neural Networks Model Nonlinear Compositionality in the Neural Representation of Human-Object Interactions?—0
DIV-FF: Dynamic Image-Video Feature Fields For Environment Understanding in Egocentric Videos—0
Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation—0
MOSE: Monocular Semantic Reconstruction Using NeRF-Lifted Noisy Priors—0
Movies2Scenes: Using Movie Metadata to Learn Scene Representation—0
Moving Beyond Navigation with Active Neural SLAM—0
Moving Object Proposals with Deep Learned Optical Flow for Video Object Segmentation—0
CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation—0
UniPLV: Towards Label-Efficient Open-World 3D Scene Understanding by Regional Visual Language Supervision—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