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 1151–1175 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
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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