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 601–650 of 1723 papers

TitleStatusHype
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models—0
DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder—0
Boundary Seeking GANs—0
Joint Optical Flow and Temporally Consistent Semantic Segmentation—0
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving—0
DreamAnywhere: Object-Centric Panoramic 3D Scene Generation—0
Bottom-up Instance Segmentation using Deep Higher-Order CRFs—0
3D Scene Understanding at Urban Intersection using Stereo Vision and Digital Map—0
Joint prototype and coefficient prediction for 3D instance segmentation—0
DORSal: Diffusion for Object-centric Representations of Scenes et al—0
DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation—0
Both Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding—0
Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs—0
Does CLIP perceive art the same way we do?—0
Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation—0
Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions—0
Do Deep Neural Networks Model Nonlinear Compositionality in the Neural Representation of Human-Object Interactions?—0
Answerability Fields: Answerable Location Estimation via Diffusion Models—0
Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World—0
Joint Modeling of Visual Objects and Relations for Scene Graph Generation—0
Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks—0
DIV-FF: Dynamic Image-Video Feature Fields For Environment Understanding in Egocentric Videos—0
Boosting Cross-spectral Unsupervised Domain Adaptation for Thermal Semantic Segmentation—0
Distraction-Aware Shadow Detection—0
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features—0
An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles—0
Distillation of Human-Object Interaction Contexts for Action Recognition—0
3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare—0
BLOS-BEV: Navigation Map Enhanced Lane Segmentation Network, Beyond Line of Sight—0
3D-Grounded Vision-Language Framework for Robotic Task Planning: Automated Prompt Synthesis and Supervised Reasoning—0
Iwin: Human-Object Interaction Detection via Transformer with Irregular Windows—0
Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition—0
Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization—0
An Exemplar-based CRF for Multi-instance Object Segmentation—0
Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses—0
BlindSpotNet: Seeing Where We Cannot See—0
Adapting to Length Shift: FlexiLength Network for Trajectory Prediction—0
iReason: Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretability—0
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation—0
Direction-Aware Semi-Dense SLAM—0
Blending Learning and Inference in Structured Prediction—0
DINeMo: Learning Neural Mesh Models with no 3D Annotations—0
A New Ratio Image Based CNN Algorithm For SAR Despeckling—0
J-MOD^2: Joint Monocular Obstacle Detection and Depth Estimation—0
Audio-Visual Collaborative Representation Learning for Dynamic Saliency Prediction—0
Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems—0
Active Scene Understanding via Online Semantic Reconstruction—0
3D Question Answering for City Scene Understanding—0
DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data—0
Diffusion Models in 3D Vision: A Survey—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