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 71–80 of 453 papers

Title	Date	Tasks	Status	Hype
Backdoor Attacks for Remote Sensing Data with Wavelet Transform	Nov 15, 2022	Backdoor Attackbackdoor defense	CodeCode Available	1
A Squeeze-and-Excitation and Transformer based Cross-task System for Environmental Sound Recognition	Mar 16, 2022	Acoustic Scene ClassificationData Augmentation	—Unverified	0
A Spatial Layout and Scale Invariant Feature Representation for Indoor Scene Classification	Jun 18, 2015	General ClassificationScene Classification	—Unverified	0
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification	Jun 20, 2017	Acoustic Scene ClassificationGeneral Classification	—Unverified	0
ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition	Oct 14, 2021	feature selectionScene Classification	—Unverified	0
A high-resolution canopy height model of the Earth	Apr 13, 2022	Decision MakingProbabilistic Deep Learning	—Unverified	0
Acoustic Scene Classification using Audio Tagging	Apr 19, 2020	Acoustic Scene ClassificationAudio Tagging	—Unverified	0
Artistic Object Recognition by Unsupervised Style Adaptation	Dec 28, 2018	Domain AdaptationObject	—Unverified	0
A Hierarchical Approach to Remote Sensing Scene Classification	Mar 29, 2021	ClassificationGeneral Classification	—Unverified	0
Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions	May 29, 2020	Acoustic Scene ClassificationClassification	—Unverified	0

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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