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 501–510 of 1723 papers

TitleStatusHype
Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection—0
ExCap3D: Expressive 3D Scene Understanding via Object Captioning with Varying Detail—0
CASPNet++: Joint Multi-Agent Motion Prediction—0
Expanding Frozen Vision-Language Models without Retraining: Towards Improved Robot Perception—0
General-Purpose Aerial Intelligent Agents Empowered by Large Language Models—0
Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks—0
Explicit3D: Graph Network with Spatial Inference for Single Image 3D Object Detection—0
Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios—0
ESGNN: Towards Equivariant Scene Graph Neural Network for 3D Scene Understanding—0
Cascaded Classification Models: Combining Models for Holistic Scene Understanding—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