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 591–600 of 1723 papers

TitleStatusHype
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models—0
Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive ReviewCode1
Moving Object Proposals with Deep Learned Optical Flow for Video Object Segmentation—0
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language ModelsCode4
InCoRo: In-Context Learning for Robotics Control with Feedback Loops—0
Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm PerspectivesCode2
SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAMCode3
Neural Language of Thought Models—0
Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation dataCode0
Non-central panorama indoor datasetCode0
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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