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 551–575 of 1723 papers

TitleStatusHype
RAFT: Robust Augmentation of FeaTures for Image Segmentation—0
Segment Any RGB-Thermal Model with Language-aided Distillation—0
Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive SegmentationCode0
Beyond Recognition: Evaluating Visual Perspective Taking in Vision Language Models—0
Embracing Diffraction: A Paradigm Shift in Wireless Sensing and Communication—0
V3LMA: Visual 3D-enhanced Language Model for Autonomous Driving—0
Category-Level and Open-Set Object Pose Estimation for Robotics—0
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding—0
TraveLLaMA: Facilitating Multi-modal Large Language Models to Understand Urban Scenes and Provide Travel Assistance—0
Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends—0
Are Vision LLMs Road-Ready? A Comprehensive Benchmark for Safety-Critical Driving Video UnderstandingCode0
Vision-Centric Representation-Efficient Fine-Tuning for Robust Universal Foreground Segmentation—0
Leveraging Automatic CAD Annotations for Supervised Learning in 3D Scene UnderstandingCode0
Temporal Propagation of Asymmetric Feature Pyramid for Surgical Scene Segmentation—0
HAECcity: Open-Vocabulary Scene Understanding of City-Scale Point Clouds with Superpoint Graph Clustering—0
Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks—0
CAGS: Open-Vocabulary 3D Scene Understanding with Context-Aware Gaussian Splatting—0
Single-Input Multi-Output Model Merging: Leveraging Foundation Models for Dense Multi-Task Learning—0
Foundation Models for Remote Sensing: An Analysis of MLLMs for Object Localization—0
DSM: Building A Diverse Semantic Map for 3D Visual Grounding—0
FMLGS: Fast Multilevel Language Embedded Gaussians for Part-level Interactive Agents—0
FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment—0
DGOcc: Depth-aware Global Query-based Network for Monocular 3D Occupancy Prediction—0
MovSAM: A Single-image Moving Object Segmentation Framework Based on Deep ThinkingCode0
RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration—0
Show:102550
← PrevPage 23 of 69Next →
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