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 1671–1680 of 1723 papers

TitleStatusHype
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer—0
Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks—0
Semantic Pose using Deep Networks Trained on Synthetic RGB-D—0
Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization—0
Lifting GIS Maps into Strong Geometric Context for Scene Understanding—0
Understanding Pedestrian Behaviors From Stationary Crowd Groups—0
Picture: A Probabilistic Programming Language for Scene Perception—0
Visual Vibrometry: Estimating Material Properties From Small Motion in Video—0
Efficient Label Collection for Unlabeled Image Datasets—0
Holistic 3D Scene Understanding From a Single Geo-Tagged Image—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