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 851–875 of 1723 papers

TitleStatusHype
Dynamic Scene Understanding from Vision-Language Representations—0
MAGIC: Mastering Physical Adversarial Generation in Context through Collaborative LLM Agents—0
Making Large Language Models Better Planners with Reasoning-Decision Alignment—0
Manhattan Scene Understanding via XSlit Imaging—0
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving—0
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition—0
MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report—0
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors—0
Dynamic Clustering Transformer Network for Point Cloud Segmentation—0
MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation—0
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding—0
DublinCity: Annotated LiDAR Point Cloud and its Applications—0
DSNet: An Efficient CNN for Road Scene Segmentation—0
Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancement—0
Adapting to Length Shift: FlexiLength Network for Trajectory Prediction—0
DSM: Building A Diverse Semantic Map for 3D Visual Grounding—0
Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry—0
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation—0
MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning—0
Active Scene Understanding via Online Semantic Reconstruction—0
A Continuous Occlusion Model for Road Scene Understanding—0
Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration—0
Unified Perception: Efficient Depth-Aware Video Panoptic Segmentation with Minimal Annotation Costs—0
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds—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