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 901–925 of 1723 papers

TitleStatusHype
Distillation of Human-Object Interaction Contexts for Action Recognition—0
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features—0
Distraction-Aware Shadow Detection—0
DIV-FF: Dynamic Image-Video Feature Fields For Environment Understanding in Egocentric Videos—0
Do Deep Neural Networks Model Nonlinear Compositionality in the Neural Representation of Human-Object Interactions?—0
Does CLIP perceive art the same way we do?—0
Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs—0
DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation—0
DORSal: Diffusion for Object-centric Representations of Scenes et al—0
DreamAnywhere: Object-Centric Panoramic 3D Scene Generation—0
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving—0
DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder—0
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models—0
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration—0
DSM: Building A Diverse Semantic Map for 3D Visual Grounding—0
DSNet: An Efficient CNN for Road Scene Segmentation—0
DublinCity: Annotated LiDAR Point Cloud and its Applications—0
Dynamic Clustering Transformer Network for Point Cloud Segmentation—0
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving—0
Dynamic Scene Understanding from Vision-Language Representations—0
Trajectory-based Scene Understanding using Dirichlet Process Mixture Model—0
DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM—0
EAGLE: Efficient Adaptive Geometry-based Learning in Cross-view Understanding—0
EarthNets: Empowering AI in Earth Observation—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