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 741–750 of 1723 papers

TitleStatusHype
Joint Optical Flow and Temporally Consistent Semantic Segmentation—0
Joint prototype and coefficient prediction for 3D instance segmentation—0
A Weakly-Supervised Depth Estimation Network Using Attention Mechanism—0
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation—0
Improving Human-Object Interaction Detection via Phrase Learning and Label Composition—0
3D Pose Regression using Convolutional Neural Networks—0
Improving Building Segmentation for Off-Nadir Satellite Imagery—0
Kestrel: Point Grounding Multimodal LLM for Part-Aware 3D Vision-Language Understanding—0
Improving 6D Object Pose Estimation of metallic Household and Industry Objects—0
Deep ensembles based on Stochastic Activation Selection for Polyp 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