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 181–190 of 453 papers

Title	Date	Tasks	Status
Adversarial Training For Sketch Retrieval	Jul 10, 2016	Image GenerationRetrieval	—Unverified
Change-point Detection Methods for Body-Worn Video	Oct 20, 2016	Change Point DetectionScene Classification	—Unverified
CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning	Jan 20, 2022	Image RetrievalRetrieval	—Unverified
An evaluation of data augmentation methods for sound scene geotagging	Oct 9, 2021	Acoustic Scene ClassificationClassification	—Unverified
Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices	Oct 18, 2021	Acoustic Scene ClassificationClassification	—Unverified
Cascade Learning Localises Discriminant Features in Visual Scene Classification	Nov 21, 2023	Classificationobject-detection	—Unverified
CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence	Dec 14, 2023	RetrievalScene Classification	—Unverified
An ensemble learning method for scene classification based on Hidden Markov Model image representation	Jul 22, 2016	Ensemble LearningGeneral Classification	—Unverified
Capturing scattered discriminative information using a deep architecture in acoustic scene classification	Jul 9, 2020	Acoustic Scene ClassificationGeneral Classification	—Unverified
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition	Jun 17, 2015	ObjectObject Recognition	—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