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 1291–1300 of 1723 papers

TitleStatusHype
Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation—0
Evaluation of Multimodal Semantic Segmentation using RGB-D Data—0
PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN—0
Input-Output Balanced Framework for Long-tailed LiDAR Semantic Segmentation—0
Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving—0
Knowledge-Guided Object Discovery with Acquired Deep ImpressionsCode0
A Comprehensive Survey of Scene Graphs: Generation and Application—0
Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions—0
Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Images—0
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning—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