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 1701–1710 of 1723 papers

Title	Date	Tasks	Status
AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery	Mar 10, 2020	Multi-Task LearningScene Understanding	CodeCode Available
3D Object Detection from Point Cloud via Voting Step Diffusion	Mar 21, 2024	3D Object DetectionObject	CodeCode Available
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields	Apr 1, 2019	3D Instance Segmentation3D Semantic Instance Segmentation	CodeCode Available
Joint stereo 3D object detection and implicit surface reconstruction	Nov 25, 2021	3D Object DetectionHallucination	CodeCode Available
Interpretable Visual Understanding with Cognitive Attention Network	Aug 6, 2021	Scene UnderstandingVisual Commonsense Reasoning	CodeCode Available
Weakly Supervised Affordance Detection	Jul 1, 2017	Affordance DetectionObject	CodeCode Available
Interactive Learning for Semantic Segmentation in Earth Observation	Sep 23, 2020	Domain AdaptationEarth Observation	CodeCode Available
Semantic Segmentation with High Inference Speed in Off-Road Environments	Apr 10, 2023	2D Semantic SegmentationAutonomous Vehicles	CodeCode Available
Cognitive TransFuser: Semantics-guided Transformer-based Sensor Fusion for Improved Waypoint Prediction	Aug 4, 2023	Imitation LearningScene Understanding	CodeCode Available
BlitzNet: A Real-Time Deep Network for Scene Understanding	Aug 9, 2017	Autonomous DrivingObject	CodeCode Available

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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