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 776–800 of 1723 papers

TitleStatusHype
SNeL: A Structured Neuro-Symbolic Language for Entity-Based Multimodal Scene Understanding—0
SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic UnderstandingCode1
InvPT++: Inverted Pyramid Multi-Task Transformer for Visual Scene UnderstandingCode2
TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem via Transformer-Based Architecture—0
A Dynamic Feature Interaction Framework for Multi-task Visual Perception—0
Towards Label-free Scene Understanding by Vision Foundation ModelsCode1
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
Towards In-context Scene UnderstandingCode1
Self-supervised Vision Transformers for 3D Pose Estimation of Novel ObjectsCode0
Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color ContrastCode1
Dynamic Clustering Transformer Network for Point Cloud Segmentation—0
Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph GenerationCode0
Multi-Scale Attention for Audio Question AnsweringCode1
Robust Category-Level 3D Pose Estimation from Synthetic Data—0
Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments—0
Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamics Audio-Visual ScenariosCode0
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer—0
Generating Visual Spatial Description via Holistic 3D Scene UnderstandingCode1
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding—0
TextSLAM: Visual SLAM with Semantic Planar Text FeaturesCode2
Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation ModelsCode1
Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene GraphsCode0
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning—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