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 1151–1175 of 1723 papers

TitleStatusHype
A Survey on Deep Learning Technique for Video SegmentationCode1
An Analysis of State-of-the-Art Models for Situated Interactive MultiModal Conversations (SIMMC)—0
Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring—0
Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization—0
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement—0
False Negative Reduction in Video Instance Segmentation using Uncertainty EstimatesCode0
SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-FlowCode0
OffRoadTranSeg: Semi-Supervised Segmentation using Transformers on OffRoad environments—0
iReason: Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretability—0
P2T: Pyramid Pooling Transformer for Scene UnderstandingCode1
EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic SegmentationCode1
Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-View TransformationCode1
OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets—0
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion—0
Part-aware Panoptic SegmentationCode1
Vision Transformers with Hierarchical AttentionCode1
Light Field Networks: Neural Scene Representations with Single-Evaluation RenderingCode1
Towards urban scenes understanding through polarization cues—0
Polarimetric Spatio-Temporal Light Transport Probing—0
Egocentric Activity Recognition and Localization on a 3D Map—0
SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data—0
Image interpretation by iterative bottom-up top-down processingCode0
Scene Understanding for Autonomous Driving—0
Lane Graph Estimation for Scene Understanding in Urban DrivingCode1
ACDC: The Adverse Conditions Dataset with Correspondences for Robust Semantic Driving Scene Perception—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