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 951–960 of 1723 papers

TitleStatusHype
Robust Multi-Modal Image Stitching for Improved Scene Understanding—0
Cloud-Device Collaborative Learning for Multimodal Large Language Models—0
BridgeNet: Comprehensive and Effective Feature Interactions via Bridge Feature for Multi-task Dense Predictions—0
Object Attribute Matters in Visual Question AnsweringCode0
AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model—0
Language-Assisted 3D Scene Understanding—0
Weakly-Supervised 3D Visual Grounding based on Visual Linguistic Alignment—0
Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis—0
Zoom in on the Plant: Fine-grained Analysis of Leaf, Stem and Vein InstancesCode0
VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene 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