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 371–380 of 1723 papers

Title	Date	Tasks	Status	Hype
AeroRIT: A New Scene for Hyperspectral Image Analysis	Dec 17, 2019	Hyperspectral image analysisImage Super-Resolution	CodeCode Available	1
From General to Specific: Informative Scene Graph Generation via Balance Adjustment	Aug 30, 2021	BlockingGraph Generation	CodeCode Available	1
Dense Audio-Visual Event Localization under Cross-Modal Consistency and Multi-Temporal Granularity Collaboration	Dec 17, 2024	audio-visual event localizationaudio-visual learning	CodeCode Available	1
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding	Dec 5, 2020	image-classificationImage Classification	CodeCode Available	1
FocusFlow: Boosting Key-Points Optical Flow Estimation for Autonomous Driving	Aug 14, 2023	Autonomous DrivingOptical Flow Estimation	CodeCode Available	1
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP	Jan 12, 2023	3D Semantic SegmentationContrastive Learning	CodeCode Available	1
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation	Dec 22, 2021	Common Sense ReasoningQuestion Answering	CodeCode Available	1
Detecting Human-Object Interaction via Fabricated Compositional Learning	Mar 15, 2021	Affordance RecognitionHuman-Object Interaction Detection	CodeCode Available	1
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation	Mar 1, 2021	3D Semantic SegmentationDecoder	CodeCode Available	1
Global Aggregation then Local Distribution in Fully Convolutional Networks	Sep 16, 2019	Instance Segmentationobject-detection	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