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 1601–1625 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
Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras—0
Predicting Deeper into the Future of Semantic SegmentationCode0
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments—0
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural NetworksCode0
Visual Translation Embedding Network for Visual Relation DetectionCode0
Boundary-Seeking Generative Adversarial NetworksCode0
Recognizing Dynamic Scenes with Deep Dual Descriptor based on Key Frames and Key Segments—0
ScanNet: Richly-annotated 3D Reconstructions of Indoor ScenesCode1
Joint 2D-3D-Semantic Data for Indoor Scene UnderstandingCode1
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper—0
Dirty Pixels: Towards End-to-End Image Processing and PerceptionCode0
Incremental Learning for Robot Perception through HRI—0
Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks—0
SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground TruthCode0
Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition—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