Scene Classification

Scene Classification is a task in which scenes from photographs are categorically classified. Unlike object classification, which focuses on classifying prominent objects in the foreground, Scene Classification uses the layout of objects within the scene, in addition to the ambient context, for classification.

Source: Scene classification with Convolutional Neural Networks

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 271–280 of 453 papers

Title	Date	Tasks	Status
Traffic scene recognition based on deep cnn and vlad spatial pyramids	Jul 24, 2017	Region ProposalScene Classification	—Unverified
Joint Spatial and Layer Attention for Convolutional Networks	Jan 16, 2019	Camera LocalizationClassification	—Unverified
Understanding Indoor Scenes Using 3D Geometric Phrases	Jun 1, 2013	General ClassificationObject	—Unverified
Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks	Sep 22, 2015	ClassificationDiversity	—Unverified
Unlocking the capabilities of explainable fewshot learning in remote sensing	Oct 12, 2023	Scene Classification	—Unverified
Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching	Apr 30, 2020	Acoustic Scene ClassificationDomain Adaptation	—Unverified
Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition	Jan 23, 2017	ClusteringGeneral Classification	—Unverified
Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer	Jan 26, 2025	Acoustic Scene ClassificationScene Classification	—Unverified
Vision-based Automated Bridge Component Recognition Integrated With High-level Scene Understanding	May 15, 2018	Scene ClassificationScene Understanding	—Unverified
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding	Jan 9, 2025	Autonomous DrivingIn-Context Learning	—Unverified

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All datasets UC Merced Land Use Dataset Places365-Standard

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	µ2Net+ (ViT-L/16)	Accuracy (%)	100	—	Unverified
2	AGOS	Accuracy (%)	99.88	—	Unverified
3	LSE-Net	Accuracy (%)	99.78	—	Unverified
4	ResNet50	Accuracy (%)	99.61	—	Unverified
5	MSMatch	Accuracy (%)	98.33	—	Unverified
6	MIDC-Net	Accuracy (%)	97.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	iSQRT-COV-Net (ResNet-50)	Top 1 Error	43.68	—	Unverified
2	WaveMix	Top 1 Error	43.55	—	Unverified