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 1376–1400 of 1723 papers

TitleStatusHype
Algorithmic Design and Implementation of Unobtrusive Multistatic Serial LiDAR Image—0
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion FramesCode1
PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation—0
Addressing the Sim2Real Gap in Robotic 3D Object Classification—0
Leveraging Auxiliary Text for Deep Recognition of Unseen Visual Relationships—0
GANspection—0
Deep Contextual Attention for Human-Object Interaction Detection—0
Deep Semantic Segmentation of Natural and Medical Images: A Review—0
Semantic Understanding of Foggy Scenes with Purely Synthetic DataCode0
Using Image Priors to Improve Scene Understanding—0
Depth-Induced Multi-Scale Recurrent Attention Network for Saliency DetectionCode0
Real-Time Semantic Stereo Matching—0
Multi-view PointNet for 3D Scene Understanding—0
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving—0
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation—0
On the Pareto Efficiency of Quantized CNN—0
Large Scale Joint Semantic Re-Localisation and Scene Understanding via Globally Unique Instance Coordinate Regression—0
Underwater Image Super-Resolution using Deep Residual MultipliersCode1
Pose-aware Multi-level Feature Network for Human Object Interaction DetectionCode0
Simultaneous Segmentation and Recognition: Towards more accurate Ego Gesture Recognition—0
Multi-Task Learning for Automotive Foggy Scene Understanding via Domain Adaptation to an Illumination-Invariant Representation—0
Visuomotor Understanding for Representation Learning of Driving Scenes—0
Global Aggregation then Local Distribution in Fully Convolutional NetworksCode1
Semantic Foreground Inpainting from Weak SupervisionCode0
DublinCity: Annotated LiDAR Point Cloud and its Applications—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