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 1626–1650 of 1723 papers

TitleStatusHype
Flow-based GAN for 3D Point Cloud Generation from a Single ImageCode0
Scene Graph Generation from Objects, Phrases and Region CaptionsCode0
Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph GenerationCode0
Auxiliary Tasks in Multi-task LearningCode0
Auto-Embedding Generative Adversarial Networks for High Resolution Image SynthesisCode0
Implicit Background Estimation for Semantic SegmentationCode0
SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground TruthCode0
Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge FindingsCode0
SceneNet: Understanding Real World Indoor Scenes With Synthetic DataCode0
Fast Scene Understanding for Autonomous DrivingCode0
Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object SearchCode0
Cognitive Visual Commonsense Reasoning Using Dynamic Working MemoryCode0
Leveraging Acoustic Images for Effective Self-Supervised Audio Representation LearningCode0
Attend, Infer, Repeat: Fast Scene Understanding with Generative ModelsCode0
A New Lightweight Hybrid Graph Convolutional Neural Network -- CNN Scheme for Scene Classification using Object Detection InferenceCode0
False Negative Reduction in Video Instance Segmentation using Uncertainty EstimatesCode0
3D Semantic Segmentation of Modular Furniture using rjMCMCCode0
Uncertainty-aware LiDAR Panoptic SegmentationCode0
Facing the Void: Overcoming Missing Data in Multi-View ImageryCode0
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural ImagesCode0
CNN-based Lidar Point Cloud De-Noising in Adverse WeatherCode0
AdaptVision: Dynamic Input Scaling in MLLMs for Versatile Scene UnderstandingCode0
An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutionsCode0
SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene UnderstandingCode0
Extremely Fine-Grained Visual Classification over Resembling Glyphs in the WildCode0
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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