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 1141–1150 of 1723 papers

TitleStatusHype
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-Training on Indoor Segmentation?—0
Scene-R1: Video-Grounded Large Language Models for 3D Scene Reasoning without 3D Annotations—0
Scene recognition based on DNN and game theory with its applications in human-robot interaction—0
SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting—0
Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames—0
SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments—0
Scene Text Detection for Augmented Reality -- Character Bigram Approach to reduce False Positive Rate—0
SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Text—0
Scene Understanding Enabled Semantic Communication with Open Channel Coding—0
Scene Understanding for Autonomous Manipulation with Deep Learning—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