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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1601–1610 of 1723 papers

TitleStatusHype
What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?—0
3D Semantic Segmentation of Modular Furniture using rjMCMCCode0
A Review on Deep Learning Techniques Applied to Semantic SegmentationCode0
Identifying First-person Camera Wearers in Third-person Videos—0
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art—0
Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks—0
Visual Vibrometry: Estimating Material Properties from Small Motions in Video—0
Visual Vibrometry: Estimating MaterialProperties from Small Motions in Video—0
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars—0
SeGAN: Segmenting and Generating the InvisibleCode0
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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