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
Segmentation Guided Attention Networks for Visual Question Answering—0
Efficient ConvNet for Real-time Semantic SegmentationCode0
Dilated Residual NetworksCode0
Towards seamless multi-view scene analysis from satellite to street-level—0
Classification of Aerial Photogrammetric 3D Point Clouds—0
DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels—0
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
Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks—0
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art—0
Visual Vibrometry: Estimating MaterialProperties from Small Motions in Video—0
Visual Vibrometry: Estimating Material Properties 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
Boundary-Seeking Generative Adversarial NetworksCode0
Visual Translation Embedding Network for Visual Relation DetectionCode0
Recognizing Dynamic Scenes with Deep Dual Descriptor based on Key Frames and Key Segments—0
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper—0
Dirty Pixels: Towards End-to-End Image Processing and PerceptionCode0
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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