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 1431–1440 of 1723 papers

Title	Date	Tasks	Status	Hype
Not Using the Car to See the Sidewalk -- Quantifying and Controlling the Effects of Context in Classification and Segmentation	Jun 1, 2019	Data AugmentationGeneral Classification	—Unverified	0
Distraction-Aware Shadow Detection	Jun 1, 2019	Scene UnderstandingShadow Detection	—Unverified	0
Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation	Jun 1, 2019	Depth EstimationMonocular Depth Estimation	—Unverified	0
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge	May 31, 2019	object-detectionObject Detection	CodeCode Available	1
Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video	May 27, 2019	Inductive BiasModel Predictive Control	CodeCode Available	0
Implicit Background Estimation for Semantic Segmentation	May 23, 2019	Scene UnderstandingSegmentation	CodeCode Available	0
Real-time Approximate Bayesian Computation for Scene Understanding	May 22, 2019	Scene Understanding	—Unverified	0
Spatial Sampling Network for Fast Scene Understanding	May 22, 2019	DecoderInstance Segmentation	—Unverified	0
Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence	May 22, 2019	Optical Flow EstimationScene Understanding	CodeCode Available	0
Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images	May 8, 2019	Domain AdaptationManagement	CodeCode Available	0

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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