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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 911–920 of 1723 papers

TitleStatusHype
Distraction-Aware Shadow Detection—0
MTANet: Multitask-Aware Network With Hierarchical Multimodal Fusion for RGB-T Urban Scene Understanding—0
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features—0
Distillation of Human-Object Interaction Contexts for Action Recognition—0
Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition—0
Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization—0
Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses—0
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation—0
Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes—0
UniQ: Unified Decoder with Task-specific Queries for Efficient Scene Graph Generation—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