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 561–570 of 1723 papers

Title	Date	Tasks	Status	Score
One model to use them all: Training a segmentation model with complementary datasets	Feb 29, 2024	AllAnatomy	CodeCode Available	5
Multi-task Planar Reconstruction with Feature Warping Guidance	Nov 25, 2023	3D ReconstructionInstance Segmentation	CodeCode Available	5
Depth-Induced Multi-Scale Recurrent Attention Network for Saliency Detection	Oct 1, 2019	RGB-D Salient Object DetectionSaliency Detection	CodeCode Available	5
Multi-task Geometric Estimation of Depth and Surface Normal from Monocular 360° Images	Nov 4, 2024	Multi-Task LearningScene Understanding	CodeCode Available	5
Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty	May 2, 2018	Scene UnderstandingSensor Fusion	CodeCode Available	5
Beyond Human Perception: Understanding Multi-Object World from Monocular View	Jan 1, 2025	3D visual groundingDenoising	CodeCode Available	5
ShelfNet for Fast Semantic Segmentation	Nov 27, 2018	Autonomous DrivingDecoder	CodeCode Available	5
An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions	Feb 20, 2019	Autonomous DrivingScene Understanding	CodeCode Available	5
DenseASPP for Semantic Segmentation in Street Scenes	Jun 1, 2018	Autonomous DrivingImage Segmentation	CodeCode Available	5
Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation	Mar 3, 2021	Autonomous DrivingDepth Estimation	CodeCode Available	5

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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