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

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Showing 1301–1350 of 1723 papers

TitleStatusHype
Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation—0
Towards Scene Understanding with Detailed 3D Object Representations—0
Towards seamless multi-view scene analysis from satellite to street-level—0
Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations—0
Towards Unseen Triples: Effective Text-Image-joint Learning for Scene Graph Generation—0
Towards urban scenes understanding through polarization cues—0
Training-Free Model Merging for Multi-target Domain Adaptation—0
TransforMerger: Transformer-based Voice-Gesture Fusion for Robust Human-Robot Communication—0
Transformers for Image-Goal Navigation—0
TraveLLaMA: Facilitating Multi-modal Large Language Models to Understand Urban Scenes and Provide Travel Assistance—0
TS40K: a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission System—0
Two Stream Scene Understanding on Graph Embedding—0
U4D: Unsupervised 4D Dynamic Scene Understanding—0
RemoteNet: Remote Sensing Image Segmentation Network based on Global-Local Information—0
Understanding and Evaluating Hallucinations in 3D Visual Language Models—0
Understanding Bayesian Rooms Using Composite 3D Object Models—0
Understanding Indoor Scenes Using 3D Geometric Phrases—0
Understanding Pedestrian Behaviors From Stationary Crowd Groups—0
Understanding Real World Indoor Scenes With Synthetic Data—0
Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks—0
Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancement—0
Unified Perception: Efficient Depth-Aware Video Panoptic Segmentation with Minimal Annotation Costs—0
Unified Representation Space for 3D Visual Grounding—0
Unified Scene Representation and Reconstruction for 3D Large Language Models—0
Uni-Fusion: Universal Continuous Mapping—0
UniGaussian: Driving Scene Reconstruction from Multiple Camera Models via Unified Gaussian Representations—0
UniPLV: Towards Label-Efficient Open-World 3D Scene Understanding by Regional Visual Language Supervision—0
UniQ: Unified Decoder with Task-specific Queries for Efficient Scene Graph Generation—0
UniRiT: Towards Few-Shot Non-Rigid Point Cloud Registration—0
Unsupervised 3D Structure Inference from Category-Specific Image Collections—0
Unsupervised Discovery and Composition of Object Light Fields—0
Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation—0
Unsupervised Foveal Vision Neural Networks with Top-Down Attention—0
Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization—0
Unveiling the Potential of iMarkers: Invisible Fiducial Markers for Advanced Robotics—0
Urban Scene Diffusion through Semantic Occupancy Map—0
User Identification: A Key Enabler for Multi-User Vision-Aided Communications—0
Using Image Priors to Improve Scene Understanding—0
V3LMA: Visual 3D-enhanced Language Model for Autonomous Driving—0
VideoGameBunny: Towards vision assistants for video games—0
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation—0
Video Token Sparsification for Efficient Multimodal LLMs in Autonomous Driving—0
Vision-based Automated Bridge Component Recognition Integrated With High-level Scene Understanding—0
Vision-Centric Representation-Efficient Fine-Tuning for Robust Universal Foreground Segmentation—0
Vision-Language Embodiment for Monocular Depth Estimation—0
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding—0
Vision-Language Models Struggle to Align Entities across Modalities—0
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding—0
Visual Affordance and Function Understanding: A Survey—0
Visual Jenga: Discovering Object Dependencies via Counterfactual Inpainting—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