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 411–420 of 1723 papers

Title	Date	Tasks	Status	Hype
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation	Oct 30, 2020	Instance SegmentationPanoptic Segmentation	CodeCode Available	1
Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model	Oct 25, 2020	Depth EstimationDepth Prediction	CodeCode Available	1
RADIATE: A Radar Dataset for Automotive Perception in Bad Weather	Oct 18, 2020	Autonomous DrivingBenchmarking	CodeCode Available	1
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning	Oct 8, 2020	Natural Language Visual GroundingScene Understanding	CodeCode Available	1
MLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene Understanding	Oct 1, 2020	Deep Learningimage-classification	CodeCode Available	1
BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments	Sep 22, 2020	Domain AdaptationScene Understanding	CodeCode Available	1
Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges	Sep 7, 2020	Scene UnderstandingSemantic Segmentation	CodeCode Available	1
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene	Aug 11, 2020	Instance SegmentationPoint Cloud Segmentation	CodeCode Available	1
Polysemy Deciphering Network for Robust Human-Object Interaction Detection	Aug 7, 2020	Human-Object Interaction DetectionObject	CodeCode Available	1
Pose-based Modular Network for Human-Object Interaction Detection	Aug 5, 2020	Human-Object Interaction DetectionObject	CodeCode Available	1

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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