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 1676–1700 of 1723 papers

TitleStatusHype
Understanding Pedestrian Behaviors From Stationary Crowd Groups—0
Picture: A Probabilistic Programming Language for Scene Perception—0
Visual Vibrometry: Estimating Material Properties From Small Motion in Video—0
Efficient Label Collection for Unlabeled Image Datasets—0
Holistic 3D Scene Understanding From a Single Geo-Tagged Image—0
Joint SFM and Detection Cues for Monocular 3D Localization in Road Scenes—0
Deeply Learned Attributes for Crowded Scene Understanding—0
SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite—0
Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models—0
SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes—0
Learning to Interpret and Describe Abstract Scenes—0
Semantic Motion Segmentation Using Dense CRF Formulation—0
The toulouse vanishing points dataset—0
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding—0
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars—0
Designing Deep Networks for Surface Normal Estimation—0
Towards Scene Understanding with Detailed 3D Object Representations—0
Scene Image is Non-Mutually Exclusive - A Fuzzy Qualitative Scene Understanding—0
Fusion Based Holistic Road Scene Understanding—0
Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding—0
An Exemplar-based CRF for Multi-instance Object Segmentation—0
Looking Beyond the Visible Scene—0
Are Cars Just 3D Boxes? - Jointly Estimating the 3D Shape of Multiple Objects—0
Incorporating Scene Context and Object Layout into Appearance Modeling—0
Scene-Independent Group Profiling in Crowd—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