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 1651–1675 of 1723 papers

TitleStatusHype
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
The Cityscapes Dataset for Semantic Urban Scene UnderstandingCode1
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
Single Image 3D Without a Single 3D Image—0
Pedestrian Travel Time Estimation in Crowded Scenes—0
Exploiting High Level Scene Cues in Stereo Reconstruction—0
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation—0
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning—0
SceneNet: Understanding Real World Indoor Scenes With Synthetic DataCode0
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer—0
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene UnderstandingCode1
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image SegmentationCode1
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
Visual Vibrometry: Estimating Material Properties From Small Motion in Video—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