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 1176–1200 of 1723 papers

TitleStatusHype
RelTransformer: A Transformer-Based Long-Tail Visual Relationship RecognitionCode1
Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory NetworksCode0
Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model—0
MonoGRNet: A General Framework for Monocular 3D Object Detection—0
SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation NetworkCode1
Single Image Depth Estimation: An Overview—0
Visiting the Invisible: Layer-by-Layer Completed Scene DecompositionCode1
Semantic Scene Completion via Integrating Instances and Scene in-the-LoopCode1
Affordance Transfer Learning for Human-Object Interaction DetectionCode1
Learning Triadic Belief Dynamics in Nonverbal Communication from VideosCode1
Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation—0
Evaluation of Multimodal Semantic Segmentation using RGB-D Data—0
Multi-View Radar Semantic SegmentationCode1
PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN—0
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D SequencesCode1
Bidirectional Projection Network for Cross Dimension Scene UnderstandingCode1
Input-Output Balanced Framework for Long-tailed LiDAR Semantic Segmentation—0
Tracking Pedestrian Heads in Dense CrowdCode1
Relation-aware Instance Refinement for Weakly Supervised Visual GroundingCode1
OFFSEG: A Semantic Segmentation Framework For Off-Road DrivingCode1
Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving—0
Knowledge-Guided Object Discovery with Acquired Deep ImpressionsCode0
A Comprehensive Survey of Scene Graphs: Generation and Application—0
Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions—0
Detecting Human-Object Interaction via Fabricated Compositional LearningCode1
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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