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 451–460 of 1723 papers

TitleStatusHype
Training-Free Model Merging for Multi-target Domain Adaptation—0
Dual-Hybrid Attention Network for Specular Highlight RemovalCode1
InfoNorm: Mutual Information Shaping of Normals for Sparse-View ReconstructionCode0
Benchmarking Vision Language Models for Cultural Understanding—0
No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen RepresentationsCode1
Shape2Scene: 3D Scene Representation Learning Through Pre-training on Shape DataCode0
Dense Multimodal Alignment for Open-Vocabulary 3D Scene Understanding—0
BLOS-BEV: Navigation Map Enhanced Lane Segmentation Network, Beyond Line of Sight—0
Pareto Low-Rank Adapters: Efficient Multi-Task Learning with Preferences—0
Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object SearchCode0
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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