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 301–310 of 453 papers

Title	Date	Tasks	Status
Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices	Mar 23, 2022	Acoustic Scene ClassificationScene Classification	—Unverified
1-D CNN based Acoustic Scene Classification via Reducing Layer-wise Dimensionality	Mar 31, 2022	Acoustic Scene ClassificationDictionary Learning	—Unverified
Zero-Shot Deep Domain Adaptation	Jul 6, 2017	ClassificationDomain Adaptation	—Unverified
Energy efficiency analysis of Spiking Neural Networks for space applications	May 16, 2025	Scene Classification	—Unverified
2D-3D Geometric Fusion Network using Multi-Neighbourhood Graph Convolution for RGB-D Indoor Scene Classification	Sep 23, 2020	General ClassificationScene Classification	—Unverified
ACGAN-based Data Augmentation Integrated with Long-term Scalogram for Acoustic Scene Classification	May 27, 2020	Acoustic Scene ClassificationData Augmentation	—Unverified
A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification	Sep 29, 2020	General Classificationimage-classification	—Unverified
A Comparative Study on Approaches to Acoustic Scene Classification using CNNs	Apr 26, 2022	Acoustic Scene ClassificationClassification	—Unverified
Acoustic Scene Classification	Nov 13, 2014	Acoustic Scene ClassificationClassification	—Unverified
Acoustic Scene Classification: A Competition Review	Aug 2, 2018	Acoustic Scene ClassificationClassification	—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