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 1051–1060 of 1723 papers

TitleStatusHype
Robust Category-Level 3D Pose Estimation from Synthetic Data—0
Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments—0
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer—0
Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamics Audio-Visual ScenariosCode0
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding—0
Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene GraphsCode0
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning—0
Transavs: End-To-End Audio-Visual Segmentation With Transformer—0
Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs—0
Self-supervised Pre-training with Masked Shape Prediction for 3D Scene Understanding—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