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 1101–1110 of 1723 papers

TitleStatusHype
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
Joint Optical Flow and Temporally Consistent Semantic Segmentation—0
Joint prototype and coefficient prediction for 3D instance segmentation—0
Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks—0
Joint SFM and Detection Cues for Monocular 3D Localization in Road Scenes—0
JUMPS: Joints Upsampling Method for Pose Sequences—0
Kestrel: Point Grounding Multimodal LLM for Part-Aware 3D Vision-Language Understanding—0
Knowledge Distillation for Incremental Learning in Semantic Segmentation—0
Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation—0
Label-Efficient LiDAR Panoptic Segmentation—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