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 971–980 of 1723 papers

TitleStatusHype
Neural Scene De-Rendering—0
Neuromorphic Visual Scene Understanding with Resonator Networks—0
Designing Deep Networks for Surface Normal Estimation—0
Newtonian Scene Understanding: Unfolding the Dynamics of Objects in Static Images—0
Next-Best-Trajectory Planning of Robot Manipulators for Effective Observation and Exploration—0
Unsupervised Foveal Vision Neural Networks with Top-Down Attention—0
NIS-SLAM: Neural Implicit Semantic RGB-D SLAM for 3D Consistent Scene Understanding—0
Design and Evaluation of Deep Learning-Based Dual-Spectrum Image Fusion Methods—0
Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization—0
Non-maximum Suppression Also Closes the Variational Approximation Gap of Multi-object Variational Autoencoders—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