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 1301–1350 of 1723 papers

TitleStatusHype
Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames—0
SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments—0
Scene Text Detection for Augmented Reality -- Character Bigram Approach to reduce False Positive Rate—0
SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Text—0
Scene Understanding Enabled Semantic Communication with Open Channel Coding—0
Scene Understanding for Autonomous Manipulation with Deep Learning—0
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
Visual Semantic Parsing: From Images to Abstract Meaning Representation—0
SDNet: Semantically Guided Depth Estimation Network—0
Visual-Semantic Scene Understanding by Sharing Labels in a Context 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
Audio-Visual Collaborative Representation Learning for Dynamic Saliency Prediction—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
Visual Traffic Knowledge Graph Generation from Scene Images—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
YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents in Augmented Reality Tasks—0
Binaural SoundNet: Predicting Semantics, Depth and Motion with Binaural Sounds—0
Visual Vibrometry: Estimating MaterialProperties from Small Motions in Video—0
Bilateral Adaptation for Human-Object Interaction Detection with Occlusion-Robustness—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
Visual Vibrometry: Estimating Material Properties From Small Motion in Video—0
Visual Vibrometry: Estimating Material Properties from Small Motions in Video—0
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer—0
Self-supervised Pre-training with Masked Shape Prediction for 3D Scene Understanding—0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
Visuomotor Understanding for Representation Learning of Driving Scenes—0
Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields—0
VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusion—0
SELMA: SEmantic Large-scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints—0
Beyond Recognition: Evaluating Visual Perspective Taking in Vision Language Models—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