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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 1591–1600 of 1723 papers

Title	Date	Tasks	Status
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks	Mar 9, 2017	Scene Understanding	CodeCode Available
MGNiceNet: Unified Monocular Geometric Scene Understanding	Nov 18, 2024	Autonomous DrivingAutonomous Vehicles	CodeCode Available
MetricGold: Leveraging Text-To-Image Latent Diffusion Models for Metric Depth Estimation	Nov 16, 2024	Depth EstimationMonocular Depth Estimation	CodeCode Available
Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision	Aug 10, 2022	3D Instance SegmentationInstance Segmentation	CodeCode Available
Improving Social Awareness Through DANTE: A Deep Affinity Network for Clustering Conversational Interactants	Jul 24, 2019	ClusteringGraph Clustering	CodeCode Available
DADA: Driver Attention Prediction in Driving Accident Scenarios	Dec 18, 2019	Driver Attention MonitoringPrediction	CodeCode Available
Structure-Aware Residual Pyramid Network for Monocular Depth Estimation	Jul 13, 2019	DecoderDepth Estimation	CodeCode Available
METEOR Guided Divergence for Video Captioning	Dec 20, 2022	Hierarchical Reinforcement LearningScene Understanding	CodeCode Available
MC-PanDA: Mask Confidence for Panoptic Domain Adaptation	Jul 19, 2024	Domain AdaptationPanoptic Segmentation	CodeCode Available
Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs	May 15, 2023	RelationScene Graph Generation	CodeCode Available

Show:10 25 50

← PrevPage 160 of 173Next →

All datasets Semantic Scene Understanding Challenge (passive actuation & ground-truth localisation)ADE20K val Semantic Scene Understanding Challenge (active actuation & ground-truth localisation)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ACRV Baseline	OMQ	0.44	—	Unverified
2	Team VGAI (TCS Research)	OMQ	0.37	—	Unverified
3	Demo_semantic_SLAM	OMQ	0.11	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CPN(ResNet-101)	Mean IoU	46.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ACRV Baseline	OMQ	0.35	—	Unverified