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 1651–1700 of 1723 papers

TitleStatusHype
A Continuous Occlusion Model for Road Scene Understanding—0
Parametric Exponential Linear Unit for Deep Convolutional Neural Networks—0
Weakly Supervised Learning of Affordances—0
Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks—0
A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding—0
Deep Structured Scene Parsing by Learning with Image Descriptions—0
Radiometric Scene Decomposition: Scene Reflectance, Illumination, and Geometry from RGB-D Images—0
Attend, Infer, Repeat: Fast Scene Understanding with Generative ModelsCode0
Friction from Reflectance: Deep Reflectance Codes for Predicting Physical Surface Properties from One-Shot In-Field Reflectance—0
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding—0
A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes—0
What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots—0
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural ImagesCode0
Car Segmentation and Pose Estimation using 3D Object Models—0
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning—0
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation—0
Exploiting High Level Scene Cues in Stereo Reconstruction—0
Single Image 3D Without a Single 3D Image—0
Pedestrian Travel Time Estimation in Crowded Scenes—0
SceneNet: Understanding Real World Indoor Scenes With Synthetic DataCode0
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer—0
Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks—0
Semantic Pose using Deep Networks Trained on Synthetic RGB-D—0
Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization—0
Lifting GIS Maps into Strong Geometric Context for Scene Understanding—0
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
Show:102550
← PrevPage 34 of 35Next →
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