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 1101–1150 of 1723 papers

TitleStatusHype
Parametric Exponential Linear Unit for Deep Convolutional Neural Networks—0
Pareto Low-Rank Adapters: Efficient Multi-Task Learning with Preferences—0
A Memory System of a Robot Cognitive Architecture and its Implementation in ArmarX—0
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation—0
AAD-LLM: Neural Attention-Driven Auditory Scene Understanding—0
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art—0
Compositional Scene Understanding through Inverse Generative Modeling—0
4D Gaussian Splatting: Modeling Dynamic Scenes with Native 4D Primitives—0
Patch2CAD: Patchwise Embedding Learning for In-the-Wild Shape Retrieval from a Single Image—0
Compositional 3D Human-Object Neural Animation—0
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds—0
CompNVS: Novel View Synthesis with Scene Completion—0
Pedestrian Travel Time Estimation in Crowded Scenes—0
Peek-a-Boo: Occlusion Reasoning in Indoor Scenes With Plane Representations—0
Video Token Sparsification for Efficient Multimodal LLMs in Autonomous Driving—0
Complete 3d relationships extraction modality alignment network for 3d dense captioning—0
PerspectiveNet: Multi-View Perception for Dynamic Scene Understanding—0
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation—0
PhyBlock: A Progressive Benchmark for Physical Understanding and Planning via 3D Block Assembly—0
PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding—0
Combining Implicit-Explicit View Correlation for Light Field Semantic Segmentation—0
Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks—0
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties—0
PhysPose: Refining 6D Object Poses with Physical Constraints—0
Picture: A Probabilistic Programming Language for Scene Perception—0
CoMatcher: Multi-View Collaborative Feature Matching—0
Cognitive Interpretation of Everyday Activities: Toward Perceptual Narrative Based Visuo-Spatial Scene Interpretation—0
Places: An Image Database for Deep Scene Understanding—0
COFGA: Classification Of Fine-Grained Features In Aerial Images—0
PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN—0
3D Vision-Language Gaussian Splatting—0
Plausible Uncertainties for Human Pose Regression—0
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples—0
CodeMapping: Real-Time Dense Mapping for Sparse SLAM using Compact Scene Representations—0
Co-AttenDWG: Co-Attentive Dimension-Wise Gating and Expert Fusion for Multi-Modal Offensive Content Detection—0
ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings—0
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction—0
Cloud-Device Collaborative Learning for Multimodal Large Language Models—0
Clock-Modeled Ternary Spatial Relations for Visual Scene Analysis—0
CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP—0
Polarimetric Spatio-Temporal Light Transport Probing—0
Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization—0
Classification of Geographical Land Structure Using Convolution Neural Network and Transfer Learning—0
Classification of Aerial Photogrammetric 3D Point Clouds—0
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments—0
3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing—0
Class-Agnostic Visio-Temporal Scene Sketch Semantic Segmentation—0
PoSeg: Pose-Aware Refinement Network for Human Instance Segmentation—0
PPTFormer: Pseudo Multi-Perspective Transformer for UAV Segmentation—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