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 1426–1450 of 1723 papers

TitleStatusHype
Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding—0
Panoptic Edge Detection—0
Neural RGB(r)D Sensing: Depth and Uncertainty From a Video Camera—0
Learning to Detect Human-Object Interactions With Knowledge—0
Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding ApproachCode0
Not Using the Car to See the Sidewalk -- Quantifying and Controlling the Effects of Context in Classification and Segmentation—0
Distraction-Aware Shadow Detection—0
Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation—0
OK-VQA: A Visual Question Answering Benchmark Requiring External KnowledgeCode1
Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from VideoCode0
Implicit Background Estimation for Semantic SegmentationCode0
Real-time Approximate Bayesian Computation for Scene Understanding—0
Spatial Sampling Network for Fast Scene Understanding—0
Bridging Stereo Matching and Optical Flow via Spatiotemporal CorrespondenceCode0
Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial ImagesCode0
A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling—0
Reasoning About Physical Interactions with Object-Centric Models—0
An Information-Theoretic Metric of Transferability for Task Transfer LearningCode0
Segmenting the FutureCode0
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation—0
Deep Optics for Monocular Depth Estimation and 3D Object Detection—0
DSNet: An Efficient CNN for Road Scene Segmentation—0
Deep Surface Normal Estimation with Hierarchical RGB-D FusionCode0
Structured agents for physical construction—0
GFF: Gated Fully Fusion for Semantic SegmentationCode1
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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