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 1431–1440 of 1723 papers

TitleStatusHype
Spatiotemporal Learning of Dynamic Gestures from 3D Point Cloud Data—0
Attention Mechanism based Cognition-level Scene Understanding—0
SpectralGaussians: Semantic, spectral 3D Gaussian splatting for multi-spectral scene representation, visualization and analysis—0
Attention-based transformer models for image captioning across languages: An in-depth survey and evaluation—0
SpeedMachines: Anytime Structured Prediction—0
Spherical World-Locking for Audio-Visual Localization in Egocentric Videos—0
Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification—0
A transition towards virtual representations of visual scenes—0
SplatTalk: 3D VQA with Gaussian Splatting—0
SpSequenceNet: Semantic Segmentation Network on 4D Point Clouds—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