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 381–390 of 1723 papers

TitleStatusHype
Diffusion-based RGB-D Semantic Segmentation with Deformable Attention Transformer—0
MOSE: Monocular Semantic Reconstruction Using NeRF-Lifted Noisy Priors—0
Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration—0
Multilateral Cascading Network for Semantic Segmentation of Large-Scale Outdoor Point Clouds—0
Hier-SLAM: Scaling-up Semantics in SLAM with a Hierarchically Categorical Gaussian SplattingCode2
CLAIR-A: Leveraging Large Language Models to Judge Audio CaptionsCode0
DAF-Net: A Dual-Branch Feature Decomposition Fusion Network with Domain Adaptive for Infrared and Visible Image FusionCode1
Towards Global Localization using Multi-Modal Object-Instance Re-IdentificationCode0
Video Token Sparsification for Efficient Multimodal LLMs in Autonomous Driving—0
DAE-Fuse: An Adaptive Discriminative Autoencoder for Multi-Modality Image Fusion—0
Show:102550
← PrevPage 39 of 173Next →
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