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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 811–820 of 1723 papers

TitleStatusHype
DEF-oriCORN: efficient 3D scene understanding for robust language-directed manipulation without demonstrations—0
A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain GapCode0
NIS-SLAM: Neural Implicit Semantic RGB-D SLAM for 3D Consistent Scene Understanding—0
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
3D Question Answering for City Scene Understanding—0
Augmented Efficiency: Reducing Memory Footprint and Accelerating Inference for 3D Semantic Segmentation through Hybrid Vision—0
Show:102550
← PrevPage 82 of 173Next →
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