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 326–350 of 1723 papers

TitleStatusHype
4D Panoptic LiDAR SegmentationCode1
DPF: Learning Dense Prediction Fields with Weak SupervisionCode1
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene UnderstandingCode1
MuirBench: A Comprehensive Benchmark for Robust Multi-image UnderstandingCode1
CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic SurgeryCode1
A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine IntelligenceCode1
MSeg: A Composite Dataset for Multi-domain Semantic SegmentationCode1
A Survey of World Models for Autonomous DrivingCode1
Affect2MM: Affective Analysis of Multimedia Content Using Emotion CausalityCode1
MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based DecodersCode1
MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question AnsweringCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
MonoDistill: Learning Spatial Features for Monocular 3D Object DetectionCode1
DIP: Unsupervised Dense In-Context Post-training of Visual RepresentationsCode1
General Geometry-aware Weakly Supervised 3D Object DetectionCode1
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion FramesCode1
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
GFF: Gated Fully Fusion for Semantic SegmentationCode1
DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object DetectionCode1
MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point CloudCode1
Global Aggregation then Local Distribution in Fully Convolutional NetworksCode1
Digging Into Self-Supervised Monocular Depth EstimationCode1
DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map ConstructionCode1
GOV-NeSF: Generalizable Open-Vocabulary Neural Semantic FieldsCode1
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIPCode1
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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