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 1631–1640 of 1723 papers

TitleStatusHype
HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group Activity Scene Graph Generation in Videos—0
FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding—0
Transavs: End-To-End Audio-Visual Segmentation With Transformer—0
HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors—0
Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments—0
Heterogeneous Visual Features Fusion via Sparse Multimodal Machine—0
HGS-Mapping: Online Dense Mapping Using Hybrid Gaussian Representation in Urban Scenes—0
Towards seamless multi-view scene analysis from satellite to street-level—0
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions—0
Algorithmic Design and Implementation of Unobtrusive Multistatic Serial LiDAR Image—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