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 401–410 of 453 papers

Title	Date	Tasks	Status
In-domain representation learning for remote sensing	Nov 15, 2019	Image ClassificationMulti-Label Image Classification	CodeCode Available
A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene Classification	Mar 29, 2022	Acoustic Scene ClassificationScene Classification	CodeCode Available
City classification from multiple real-world sound scenes	Jul 29, 2019	Acoustic Scene ClassificationClassification	CodeCode Available
All grains, one scheme (AGOS): Learning multigrain instance representation for aerial scene classification	Aug 25, 2022	Aerial Scene ClassificationAll	CodeCode Available
Centroid Based Concept Learning for RGB-D Indoor Scene Classification	Nov 1, 2019	ClassificationClustering	CodeCode Available
Visual and audio scene classification for detecting discrepancies in video: a baseline method and experimental protocol	May 1, 2024	Scene Classification	CodeCode Available
Unsupervised adversarial domain adaptation for acoustic scene classification	Aug 17, 2018	Acoustic Scene ClassificationClassification	CodeCode Available
Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs	Oct 4, 2016	General ClassificationScene Classification	CodeCode Available
Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing	Jun 2, 2024	image-classificationImage Classification	CodeCode Available
Object Detectors Emerge in Deep Scene CNNs	Dec 22, 2014	General ClassificationObject	CodeCode Available

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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