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 71–80 of 1723 papers

TitleStatusHype
Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind—0
SEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving—0
TinyRS-R1: Compact Multimodal Language Model for Remote Sensing—0
StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story GenerationCode1
APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point CloudsCode0
Seeing Beyond the Scene: Enhancing Vision-Language Models with Interactional Reasoning—0
DRRNet: Macro-Micro Feature Fusion and Dual Reverse Refinement for Camouflaged Object DetectionCode0
Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous DrivingCode1
Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods,Datasets,and Future Directions—0
Boosting Cross-spectral Unsupervised Domain Adaptation for Thermal Semantic 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