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 801–850 of 1723 papers

TitleStatusHype
Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation—0
A Multiple-View Geometric Model for Specularity Prediction on General Curved Surfaces—0
Learning to Detect Human-Object Interactions With Knowledge—0
Learning to Exploit Stability for 3D Scene Parsing—0
Learning to Interpret and Describe Abstract Scenes—0
Multimodal 3D Object Detection on Unseen Domains—0
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement—0
Image-to-Height Domain Translation for Synthetic Aperture Sonar—0
Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery—0
Leverage Cross-Attention for End-to-End Open-Vocabulary Panoptic Reconstruction—0
Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization—0
An Exemplar-based CRF for Multi-instance Object Segmentation—0
Leveraging Auxiliary Text for Deep Recognition of Unseen Visual Relationships—0
Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems—0
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation—0
A Comprehensive Review of Modern Object Segmentation Approaches—0
Image Parsing with Stochastic Scene Grammar—0
Deep Contextual Attention for Human-Object Interaction Detection—0
Lifting GIS Maps into Strong Geometric Context for Scene Understanding—0
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding—0
Image-Graph-Image Translation via Auto-Encoding—0
A model of saliency-based visual attention for rapid scene analysis—0
Multimodal 3D Reasoning Segmentation with Complex Scenes—0
LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment—0
Living in a Material World: Learning Material Properties from Full-Waveform Flash Lidar Data for Semantic Segmentation—0
Image Captioning with Integrated Bottom-Up and Multi-level Residual Top-Down Attention for Game Scene Understanding—0
Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks—0
LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness—0
LLaVA-4D: Embedding SpatioTemporal Prompt into LMMs for 4D Scene Understanding—0
IM2CAD—0
A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features—0
MTANet: Multitask-Aware Network With Hierarchical Multimodal Fusion for RGB-T Urban Scene Understanding—0
AVD2: Accident Video Diffusion for Accident Video Description—0
Moving Object Proposals with Deep Learned Optical Flow for Video Object Segmentation—0
Identifying First-person Camera Wearers in Third-person Videos—0
AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving—0
CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation—0
Long Range Pooling for 3D Large-Scale Scene Understanding—0
DAWN: Vehicle Detection in Adverse Weather Nature Dataset—0
MOSE: Monocular Semantic Reconstruction Using NeRF-Lifted Noisy Priors—0
Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs—0
Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation—0
DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation—0
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding—0
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars—0
LVLM-empowered Multi-modal Representation Learning for Visual Place Recognition—0
A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM—0
Movies2Scenes: Using Movie Metadata to Learn Scene Representation—0
HyKo: A Spectral Dataset for Scene Understanding—0
A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding—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