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 601–625 of 1723 papers

TitleStatusHype
Object Attribute Matters in Visual Question AnsweringCode0
One model to use them all: Training a segmentation model with complementary datasetsCode0
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance VotingCode0
DADA: Driver Attention Prediction in Driving Accident ScenariosCode0
Multi-task Planar Reconstruction with Feature Warping GuidanceCode0
Neural Radiance Field CodebooksCode0
Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene GraphsCode0
CrossModalityDiffusion: Multi-Modal Novel View Synthesis with Unified Intermediate RepresentationCode0
Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label UncertaintyCode0
Auto-Embedding Generative Adversarial Networks for High Resolution Image SynthesisCode0
ShelfNet for Fast Semantic SegmentationCode0
Multi-task Geometric Estimation of Depth and Surface Normal from Monocular 360° ImagesCode0
Neural RGB->D Sensing: Depth and Uncertainty from a Video CameraCode0
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and ClassificationCode0
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose EstimationCode0
MovSAM: A Single-image Moving Object Segmentation Framework Based on Deep ThinkingCode0
Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR ScansCode0
Grid-augmented vision: A simple yet effective approach for enhanced spatial understanding in multi-modal agentsCode0
Continual Learning of Unsupervised Monocular Depth from VideosCode0
Monocular 3D Object Detection with Pseudo-LiDAR Point CloudCode0
Model-based inexact graph matching on top of CNNs for semantic scene understandingCode0
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object RepresentationsCode0
MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object LocalizationCode0
MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and ModalitiesCode0
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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