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 1501–1510 of 1723 papers

TitleStatusHype
Incorporating Luminance, Depth and Color Information by a Fusion-based Network for Semantic SegmentationCode0
A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM—0
Context-Dependent Diffusion Network for Visual Relationship Detection—0
Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions—0
On the Importance of Visual Context for Data Augmentation in Scene Understanding—0
Modeling human intuitions about liquid flow with particle-based simulation—0
Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?Code0
BOLD5000: A public fMRI dataset of 5000 imagesCode0
Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene ImagesCode0
Multiple-gaze geometry: Inferring novel 3D locations from gazes observed in monocular video—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