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 501–510 of 1723 papers

TitleStatusHype
MTVQA: Benchmarking Multilingual Text-Centric Visual Question AnsweringCode2
Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imageryCode1
Grounded 3D-LLM with Referent TokensCode2
A Preprocessing and Postprocessing Voxel-based Method for LiDAR Semantic Segmentation Improvement in Long Distance—0
When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language ModelsCode7
4D Panoptic Scene Graph GenerationCode3
3D Shape Augmentation with Content-Aware Shape Resizing—0
BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation—0
DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map ConstructionCode1
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for ControlCode1
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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