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 1651–1675 of 1723 papers

TitleStatusHype
Holistic 3D Scene Understanding From a Single Geo-Tagged Image—0
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset—0
Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense—0
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding—0
Holistic Understanding of 3D Scenes as Universal Scene Description—0
HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for Autonomous Driving—0
HSPACE: Synthetic Parametric Humans Animated in Complex Environments—0
HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting—0
Human Action Recognition in Still Images Using ConViT—0
Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection—0
Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding—0
HUNTER: Unsupervised Human-centric 3D Detection via Transferring Knowledge from Synthetic Instances to Real Scenes—0
HUSH: Holistic Panoramic 3D Scene Understanding using Spherical Harmonics—0
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface—0
Hybrid Optimized Deep Convolution Neural Network based Learning Model for Object Detection—0
Hybrid Primal Sketch: Combining Analogy, Qualitative Representations, and Computer Vision for Scene Understanding—0
HyKo: A Spectral Dataset for Scene Understanding—0
Exploring Deep 3D Spatial Encodings for Large-Scale 3D Scene Understanding—0
Towards urban scenes understanding through polarization cues—0
Explore and Tell: Embodied Visual Captioning in 3D Environments—0
A Kinematic Bottleneck Approach For Pose Regression of Flexible Surgical Instruments directly from Images—0
Exploiting the ConvLSTM: Human Action Recognition using Raw Depth Video-Based Recurrent Neural Networks—0
A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling—0
Identifying First-person Camera Wearers in Third-person Videos—0
Exploiting Temporal Coherence for Multi-modal Video Categorization—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