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 431–440 of 1723 papers

TitleStatusHype
Complete 3d relationships extraction modality alignment network for 3d dense captioning—0
A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain GapCode0
DEF-oriCORN: efficient 3D scene understanding for robust language-directed manipulation without demonstrations—0
NIS-SLAM: Neural Implicit Semantic RGB-D SLAM for 3D Consistent Scene Understanding—0
Dynamic Scene Understanding through Object-Centric Voxelization and Neural RenderingCode1
From Feature Importance to Natural Language Explanations Using LLMs with RAGCode0
Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets—0
ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention UnderstandingCode0
GP-VLS: A general-purpose vision language model for surgery—0
Answerability Fields: Answerable Location Estimation via Diffusion Models—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