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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 776–800 of 1723 papers

TitleStatusHype
Image Parsing with Stochastic Scene Grammar—0
Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey—0
Learning-based Relational Object Matching Across Views—0
Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation—0
Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes—0
Learning Depth from Single Images with Deep Neural Network Embedding Focal Length—0
Learning Direct Optimization for Scene Understanding—0
Deep Contextual Attention for Human-Object Interaction Detection—0
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding—0
Image-Graph-Image Translation via Auto-Encoding—0
Learning Geometric-Aware Properties in 2D Representation Using Lightweight CAD Models, or Zero Real 3D Pairs—0
A model of saliency-based visual attention for rapid scene analysis—0
MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation—0
Learning in Audio-visual Context: A Review, Analysis, and New Perspective—0
DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data—0
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding—0
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation—0
Image Captioning with Integrated Bottom-Up and Multi-level Residual Top-Down Attention for Game Scene Understanding—0
Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks—0
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition—0
IM2CAD—0
Audio-Visual Collaborative Representation Learning for Dynamic Saliency Prediction—0
3D Question Answering for City Scene Understanding—0
A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features—0
MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report—0
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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