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 451–475 of 1723 papers

TitleStatusHype
Image Segmentation Using Deep Learning: A SurveyCode1
NODIS: Neural Ordinary Differential Scene UnderstandingCode1
Visual-Semantic Graph Attention Networks for Human-Object Interaction DetectionCode1
IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal EstimationCode1
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
TextSLAM: Visual SLAM with Planar Text FeaturesCode1
Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GANCode1
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion FramesCode1
Underwater Image Super-Resolution using Deep Residual MultipliersCode1
Global Aggregation then Local Distribution in Fully Convolutional NetworksCode1
Dynamic Graph Message Passing NetworksCode1
VideoNavQA: Bridging the Gap between Visual and Embodied Question AnsweringCode1
M3D-RPN: Monocular 3D Region Proposal Network for Object DetectionCode1
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation NetworkCode1
OK-VQA: A Visual Question Answering Benchmark Requiring External KnowledgeCode1
GFF: Gated Fully Fusion for Semantic SegmentationCode1
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene UnderstandingCode1
Unified Perceptual Parsing for Scene UnderstandingCode1
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoningCode1
Digging Into Self-Supervised Monocular Depth EstimationCode1
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny ObjectsCode1
Semantic Line Detection and Its ApplicationsCode1
LinkNet: Exploiting Encoder Representations for Efficient Semantic SegmentationCode1
ScanNet: Richly-annotated 3D Reconstructions of Indoor ScenesCode1
Joint 2D-3D-Semantic Data for Indoor Scene UnderstandingCode1
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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