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 1641–1650 of 1723 papers

TitleStatusHype
Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery—0
Newtonian Scene Understanding: Unfolding the Dynamics of Objects in Static Images—0
Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition—0
Occlusion Boundary Detection via Deep Exploration of Context—0
A Task-Oriented Approach for Cost-Sensitive Recognition—0
Understanding Real World Indoor Scenes With Synthetic Data—0
A Continuous Occlusion Model for Road Scene Understanding—0
Parametric Exponential Linear Unit for Deep Convolutional Neural Networks—0
Weakly Supervised Learning of Affordances—0
Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks—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