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 971–980 of 1723 papers

TitleStatusHype
Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection—0
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding—0
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset—0
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast—0
Factored Neural Representation for Scene Understanding—0
Factor Graph based 3D Multi-Object Tracking in Point Clouds—0
Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments—0
FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding—0
FastLGS: Speeding up Language Embedded Gaussians with Feature Grid Mapping—0
Fast Neural Architecture Search for Lightweight Dense Prediction 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