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 1041–1050 of 1723 papers

TitleStatusHype
Neural Projection Mapping Using Reflectance Fields—0
SNeL: A Structured Neuro-Symbolic Language for Entity-Based Multimodal Scene Understanding—0
A Dynamic Feature Interaction Framework for Multi-task Visual Perception—0
TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem via Transformer-Based Architecture—0
Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses—0
Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing—0
Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes—0
Self-supervised Vision Transformers for 3D Pose Estimation of Novel ObjectsCode0
Dynamic Clustering Transformer Network for Point Cloud Segmentation—0
Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph GenerationCode0
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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