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 1681–1690 of 1723 papers

TitleStatusHype
Joint SFM and Detection Cues for Monocular 3D Localization in Road Scenes—0
Deeply Learned Attributes for Crowded Scene Understanding—0
SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite—0
Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models—0
SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes—0
Learning to Interpret and Describe Abstract Scenes—0
Semantic Motion Segmentation Using Dense CRF Formulation—0
The toulouse vanishing points dataset—0
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding—0
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars—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