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 1311–1320 of 1723 papers

TitleStatusHype
Visual Semantic Parsing: From Images to Abstract Meaning Representation—0
SDNet: Semantically Guided Depth Estimation Network—0
Visual-Semantic Scene Understanding by Sharing Labels in a Context Network—0
SE(3) Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation—0
SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles—0
Audio-Visual Collaborative Representation Learning for Dynamic Saliency Prediction—0
SeasoNet: A Seasonal Scene Classification, segmentation and Retrieval dataset for satellite Imagery over Germany—0
Second-order Democratic Aggregation—0
Neural Groundplans: Persistent Neural Scene Representations from a Single Image—0
Seeing Beyond Classes: Zero-Shot Grounded Situation Recognition via Language Explainer—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