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 1671–1680 of 1723 papers

Title	Date	Tasks	Status
Segmenting the Future	Apr 24, 2019	Autonomous DrivingDecision Making	CodeCode Available
Learning Regional Purity for Instance Segmentation on 3D Point Clouds	Nov 3, 2020	3D Instance Segmentation3D Semantic Segmentation	CodeCode Available
SeG-SR: Integrating Semantic Knowledge into Remote Sensing Image Super-Resolution via Vision-Language Model	May 29, 2025	Image Super-ResolutionLanguage Modeling	CodeCode Available
Learning Panoptic Segmentation from Instance Contours	Oct 16, 2020	ClusteringInstance Segmentation	CodeCode Available
Box for Mask and Mask for Box: weak losses for multi-task partially supervised learning	Nov 26, 2024	Objectobject-detection	CodeCode Available
Are Vision LLMs Road-Ready? A Comprehensive Benchmark for Safety-Critical Driving Video Understanding	Apr 20, 2025	Autonomous DrivingImage Captioning	CodeCode Available
Efficient Computation Sharing for Multi-Task Visual Scene Understanding	Mar 16, 2023	Multi-Task LearningScene Understanding	CodeCode Available
DualMLP: a two-stream fusion model for 3D point cloud classification	Oct 10, 2023	3D Point Cloud ClassificationPoint Cloud Classification	CodeCode Available
Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation	Mar 31, 2019	Autonomous Drivingroad scene understanding	CodeCode Available
Self-Supervised Partial Cycle-Consistency for Multi-View Matching	Jan 10, 2025	Scene Understanding	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