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 1331–1340 of 1723 papers

TitleStatusHype
Real-time Semantic Segmentation with Context Aggregation Network—0
Axiom Learning and Belief Tracing for Transparent Decision Making in Robotics—0
Unsupervised Foveal Vision Neural Networks with Top-Down Attention—0
Learning Panoptic Segmentation from Instance ContoursCode0
DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM—0
Constructing a Visual Relationship Authenticity DatasetCode0
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer—0
Weakly Supervised Learning of Multi-Object 3D Scene Decompositions Using Deep Shape Priors—0
Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph ConsensusCode0
Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation—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