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 1311–1320 of 1723 papers

TitleStatusHype
BUTLER: Building Understanding in TextWorld via Language for Embodied Reasoning—0
Non-maximum Suppression Also Closes the Variational Approximation Gap of Multi-object Variational Autoencoders—0
Pseudo Label-Guided Multi Task Learning for Scene Understanding—0
Scene Text Detection for Augmented Reality -- Character Bigram Approach to reduce False Positive Rate—0
P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding—0
Classification of Single-View Object Point Clouds—0
Embodied Visual Active Learning for Semantic Segmentation—0
Practical Auto-Calibration for Spatial Scene-Understanding from Crowdsourced Dashcamera Videos—0
Image-Graph-Image Translation via Auto-Encoding—0
Multi-Model Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation—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