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 1381–1390 of 1723 papers

TitleStatusHype
A Weakly-Supervised Depth Estimation Network Using Attention Mechanism—0
A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features—0
Sensor Adaptation for Improved Semantic Segmentation of Overhead Imagery—0
Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning—0
SEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving—0
3D-MVP: 3D Multiview Pretraining for Robotic Manipulation—0
3D-MVP: 3D Multiview Pretraining for Manipulation—0
SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction—0
AVD2: Accident Video Diffusion for Accident Video Description—0
Shallow2Deep: Indoor Scene Modeling by Single Image Understanding—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