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 51–75 of 1723 papers

TitleStatusHype
Compositional Scene Understanding through Inverse Generative Modeling—0
Right Side Up? Disentangling Orientation Understanding in MLLMs with Fine-grained Multi-axis Perception Tasks—0
A Graph Completion Method that Jointly Predicts Geometry and Topology Enables Effective Molecule Assembly—0
OccLE: Label-Efficient 3D Semantic Occupancy Prediction—0
OmniIndoor3D: Comprehensive Indoor 3D Reconstruction—0
Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancement—0
FHGS: Feature-Homogenized Gaussian Splatting—0
Co-AttenDWG: Co-Attentive Dimension-Wise Gating and Expert Fusion for Multi-Modal Offensive Content Detection—0
Can MLLMs Guide Me Home? A Benchmark Study on Fine-Grained Visual Reasoning from Transit Maps—0
Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding—0
From Flight to Insight: Semantic 3D Reconstruction for Aerial Inspection via Gaussian Splatting and Language-Guided Segmentation—0
Assessing the generalization performance of SAM for ureteroscopy scene understanding—0
CoNav: Collaborative Cross-Modal Reasoning for Embodied NavigationCode1
Robo2VLM: Visual Question Answering from Large-Scale In-the-Wild Robot Manipulation Datasets—0
DC-Scene: Data-Centric Learning for 3D Scene UnderstandingCode0
RAZER: Robust Accelerated Zero-Shot 3D Open-Vocabulary Panoptic Reconstruction with Spatio-Temporal Aggregation—0
HAMF: A Hybrid Attention-Mamba Framework for Joint Scene Context Understanding and Future Motion Representation Learning—0
AdaToken-3D: Dynamic Spatial Gating for Efficient 3D Large Multimodal-Models Reasoning—0
Predicting Reaction Time to Comprehend Scenes with Foveated Scene Understanding Maps—0
LLaVA-4D: Embedding SpatioTemporal Prompt into LMMs for 4D Scene Understanding—0
SEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving—0
Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind—0
TinyRS-R1: Compact Multimodal Language Model for Remote Sensing—0
APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point CloudsCode0
StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story GenerationCode1
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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