SOTAVerified

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

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Showing 301–325 of 1723 papers

TitleStatusHype
From General to Specific: Informative Scene Graph Generation via Balance AdjustmentCode1
General Geometry-aware Weakly Supervised 3D Object DetectionCode1
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D UnderstandingCode1
A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed ImagesCode1
A Survey on Deep Learning Technique for Video SegmentationCode1
4D Panoptic LiDAR SegmentationCode1
A Two-Stage Masked Autoencoder Based Network for Indoor Depth CompletionCode1
Context Prior for Scene SegmentationCode1
CSFNet: A Cosine Similarity Fusion Network for Real-Time RGB-X Semantic Segmentation of Driving ScenesCode1
Few-Shot Object Detection and Viewpoint Estimation for Objects in the WildCode1
CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic SurgeryCode1
Human-centric Scene Understanding for 3D Large-scale ScenariosCode1
A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine IntelligenceCode1
IDA-3D: Instance-Depth-Aware 3D Object Detection From Stereo Vision for Autonomous DrivingCode1
CoPeD-Advancing Multi-Robot Collaborative Perception: A Comprehensive Dataset in Real-World EnvironmentsCode1
Image Segmentation Using Deep Learning: A SurveyCode1
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene UnderstandingCode1
A Survey of World Models for Autonomous DrivingCode1
Affect2MM: Affective Analysis of Multimedia Content Using Emotion CausalityCode1
Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous DrivingCode1
AutoInst: Automatic Instance-Based Segmentation of LiDAR 3D ScansCode1
Cross-Modal and Uncertainty-Aware Agglomeration for Open-Vocabulary 3D Scene UnderstandingCode1
KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3DCode1
Knowledge Distillation from 3D to Bird's-Eye-View for LiDAR Semantic SegmentationCode1
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene UnderstandingCode1
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All datasetsSemantic Scene Understanding Challenge (passive actuation & ground-truth localisation)ADE20K valSemantic Scene Understanding Challenge (active actuation & ground-truth localisation)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.44—Unverified
2Team VGAI (TCS Research)OMQ0.37—Unverified
3Demo_semantic_SLAMOMQ0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1CPN(ResNet-101)Mean IoU46.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.35—Unverified