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 1341–1350 of 1723 papers

TitleStatusHype
Semi-Supervised Learning of Multi-Object 3D Scene Representations—0
A Survey on Deep Learning Methods for Semantic Image Segmentation in Real-Time—0
Towards General Purpose Geometry-Preserving Single-View Depth Estimation—0
Interactive Learning for Semantic Segmentation in Earth ObservationCode0
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation—0
On the Structures of Representation for the Robustness of Semantic Segmentation to Input CorruptionCode0
Deep Learning Techniques for Geospatial Data Analysis—0
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds—0
TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module—0
m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural NetworksCode0
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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