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 1151–1200 of 1723 papers

TitleStatusHype
Scene Understanding for Autonomous Driving—0
Scene Understanding in Pick-and-Place Tasks: Analyzing Transformations Between Initial and Final Scenes—0
Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System—0
SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding—0
SDNet: Semantically Guided Depth Estimation Network—0
SE(3) Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation—0
SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles—0
SeasoNet: A Seasonal Scene Classification, segmentation and Retrieval dataset for satellite Imagery over Germany—0
Second-order Democratic Aggregation—0
Neural Groundplans: Persistent Neural Scene Representations from a Single Image—0
Seeing Beyond Classes: Zero-Shot Grounded Situation Recognition via Language Explainer—0
Seeing Beyond the Scene: Enhancing Vision-Language Models with Interactional Reasoning—0
Seeing the Signs: A Survey of Edge-Deployable OCR Models for Billboard Visibility Analysis—0
Seeing with Humans: Gaze-Assisted Neural Image Captioning—0
Seeing With Sound: Long-range Acoustic Beamforming for Multimodal Scene Understanding—0
Segment Any 3D Gaussians—0
Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation—0
Segment Any RGB-Thermal Model with Language-aided Distillation—0
Segment Anything, Even Occluded—0
Segmentation Guided Attention Networks for Visual Question Answering—0
Segmentation-guided Domain Adaptation for Efficient Depth Completion—0
Segment-Fusion: Hierarchical Context Fusion for Robust 3D Semantic Segmentation—0
Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding—0
Self-supervised Learning of Occlusion Aware Flow Guided 3D Geometry Perception with Adaptive Cross Weighted Loss from Monocular Videos—0
Self-supervised Learning via Cluster Distance Prediction for Operating Room Context Awareness—0
Self-Supervised Object Detection from Egocentric Videos—0
Self-supervised Pre-training with Masked Shape Prediction for 3D Scene Understanding—0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints—0
Semantic Augmented Reality Environment with Material-Aware Physical Interactions—0
Semantic-aware Transmission for Robust Point Cloud Classification—0
Semantic Dense Reconstruction with Consistent Scene Segments—0
Semantic Detection of Potential Wind-borne Debris in Construction Jobsites: Digital Twining for Hurricane Preparedness and Jobsite Safety—0
SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments—0
Semantic Foggy Scene Understanding with Synthetic Data—0
Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting—0
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer—0
Semantic Is Enough: Only Semantic Information For NeRF Reconstruction—0
Semantic Motion Segmentation Using Dense CRF Formulation—0
Semantic Pose using Deep Networks Trained on Synthetic RGB-D—0
Semantic segmentation of surgical hyperspectral images under geometric domain shifts—0
SemanticSplat: Feed-Forward 3D Scene Understanding with Language-Aware Gaussian Fields—0
Semi-supervised and Deep learning Frameworks for Video Classification and Key-frame Identification—0
Semi-Supervised Learning of Multi-Object 3D Scene Representations—0
Weakly Supervised Learning of Multi-Object 3D Scene Decompositions Using Deep Shape Priors—0
Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation—0
Semi-Supervised Semantic Mapping through Label Propagation with Semantic Texture Meshes—0
Semi-supervised Video Semantic Segmentation Using Unreliable Pseudo Labels for PVUW2024—0
Sensor Adaptation for Improved Semantic Segmentation of Overhead Imagery—0
Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning—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