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 1071–1080 of 1723 papers

TitleStatusHype
Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems—0
Cross-modal Learning for Multi-modal Video Categorization—0
Panoptic Edge Detection—0
Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving—0
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation—0
P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding—0
PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing—0
PADriver: Towards Personalized Autonomous Driving—0
PAg-NeRF: Towards fast and efficient end-to-end panoptic 3D representations for agricultural robotics—0
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer—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