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 931–940 of 1723 papers

TitleStatusHype
Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems—0
AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents—0
S^3M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving—0
ICGNet: A Unified Approach for Instance-Centric GraspingCode0
BPDO:Boundary Points Dynamic Optimization for Arbitrary Shape Scene Text Detection—0
SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding—0
Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization—0
Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation—0
Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection—0
VLP: Vision Language Planning for Autonomous Driving—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