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 1301–1310 of 1723 papers

TitleStatusHype
Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation—0
Towards Scene Understanding with Detailed 3D Object Representations—0
Towards seamless multi-view scene analysis from satellite to street-level—0
Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations—0
Towards Unseen Triples: Effective Text-Image-joint Learning for Scene Graph Generation—0
Towards urban scenes understanding through polarization cues—0
Training-Free Model Merging for Multi-target Domain Adaptation—0
TransforMerger: Transformer-based Voice-Gesture Fusion for Robust Human-Robot Communication—0
Transformers for Image-Goal Navigation—0
TraveLLaMA: Facilitating Multi-modal Large Language Models to Understand Urban Scenes and Provide Travel Assistance—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