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 1701–1723 of 1723 papers

TitleStatusHype
SpeedMachines: Anytime Structured Prediction—0
Visual-Semantic Scene Understanding by Sharing Labels in a Context Network—0
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations—0
Cognitive Interpretation of Everyday Activities: Toward Perceptual Narrative Based Visuo-Spatial Scene Interpretation—0
A Sentence Is Worth a Thousand Pixels—0
Beyond Point Clouds: Scene Understanding by Reasoning Geometry and Physics—0
Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs—0
Understanding Bayesian Rooms Using Composite 3D Object Models—0
Manhattan Scene Understanding via XSlit Imaging—0
Heterogeneous Visual Features Fusion via Sparse Multimodal Machine—0
Hallucinated Humans as the Hidden Context for Labeling 3D Scenes—0
Understanding Indoor Scenes Using 3D Geometric Phrases—0
Clock-Modeled Ternary Spatial Relations for Visual Scene Analysis—0
Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World—0
Blending Learning and Inference in Structured Prediction—0
-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding—0
Image Parsing with Stochastic Scene Grammar—0
Parsing Natural Scenes and Natural Language with Recursive Neural NetworksCode0
Large Margin Learning of Upstream Scene Understanding Models—0
Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models—0
Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships—0
Cascaded Classification Models: Combining Models for Holistic Scene Understanding—0
A model of saliency-based visual attention for rapid scene analysis—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