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 461–470 of 1723 papers

Title	Date	Tasks	Status	Hype
Dynamic Graph Message Passing Networks	Aug 19, 2019	Image Classificationobject-detection	CodeCode Available	1
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering	Aug 14, 2019	Embodied Question AnsweringQuestion Answering	CodeCode Available	1
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection	Jul 13, 2019	3D Object Detection3D Object Detection From Monocular Images	CodeCode Available	1
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network	Jul 8, 2019	3D Object DetectionObject	CodeCode Available	1
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge	May 31, 2019	object-detectionObject Detection	CodeCode Available	1
GFF: Gated Fully Fusion for Semantic Segmentation	Apr 3, 2019	Scene ParsingScene Understanding	CodeCode Available	1
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding	Jan 5, 2019	Domain AdaptationScene Understanding	CodeCode Available	1
Unified Perceptual Parsing for Scene Understanding	Jul 26, 2018	2D Semantic SegmentationScene Understanding	CodeCode Available	1
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning	Jul 16, 2018	3d scene graph generationGraph Generation	CodeCode Available	1
Digging Into Self-Supervised Monocular Depth Estimation	Jun 4, 2018	Camera Pose EstimationDepth Estimation	CodeCode Available	1

Show:10 25 50

← PrevPage 47 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