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 1261–1270 of 1723 papers

TitleStatusHype
TADFormer: Task-Adaptive Dynamic TransFormer for Efficient Multi-Task Learning—0
Talk-to-Resolve: Combining scene understanding and spatial dialogue to resolve granular task ambiguity for a collocated robot—0
TanDepth: Leveraging Global DEMs for Metric Monocular Depth Estimation in UAVs—0
TARS: Traffic-Aware Radar Scene Flow Estimation—0
Taskology: Utilizing Task Relations at Scale—0
TB-HSU: Hierarchical 3D Scene Understanding with Contextual Affordances—0
Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Leveraging Color Shift Correction, RoPE-Swin Backbone, and Quantile-based Label Denoising Strategy for Robust Outdoor Scene Understanding—0
Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction—0
Temporal Propagation of Asymmetric Feature Pyramid for Surgical Scene Segmentation—0
Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation—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