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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 801–810 of 1723 papers

TitleStatusHype
Transavs: End-To-End Audio-Visual Segmentation With Transformer—0
Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and BeyondCode1
Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs—0
Self-supervised Pre-training with Masked Shape Prediction for 3D Scene Understanding—0
Living in a Material World: Learning Material Properties from Full-Waveform Flash Lidar Data for Semantic Segmentation—0
Learning-based Relational Object Matching Across Views—0
ArK: Augmented Reality with Knowledge Interactive Emergent Ability—0
TaskPrompter: Spatial-Channel Multi-Task Prompting for Dense Scene UnderstandingCode2
DynaVol: Unsupervised Learning for Dynamic Scenes through Object-Centric VoxelizationCode1
Neural Implicit Dense Semantic SLAM—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