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 611–620 of 1723 papers

TitleStatusHype
DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation—0
Both Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding—0
Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs—0
Does CLIP perceive art the same way we do?—0
Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation—0
Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions—0
Do Deep Neural Networks Model Nonlinear Compositionality in the Neural Representation of Human-Object Interactions?—0
Answerability Fields: Answerable Location Estimation via Diffusion Models—0
Incorporating Scene Context and Object Layout into Appearance Modeling—0
Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs—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