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 861–870 of 1723 papers

TitleStatusHype
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding—0
DublinCity: Annotated LiDAR Point Cloud and its Applications—0
DSNet: An Efficient CNN for Road Scene Segmentation—0
Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancement—0
Adapting to Length Shift: FlexiLength Network for Trajectory Prediction—0
DSM: Building A Diverse Semantic Map for 3D Visual Grounding—0
Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry—0
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation—0
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning—0
Active Scene Understanding via Online Semantic Reconstruction—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