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 151–160 of 453 papers

Title	Date	Tasks	Status
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes	May 29, 2023	Acoustic Scene ClassificationContinual Learning	—Unverified
Human-annotated label noise and their impact on ConvNets for remote sensing image scene classification	May 20, 2023	Scene Classification	—Unverified
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms	May 16, 2023	Acoustic Scene ClassificationScene Classification	—Unverified
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit	May 6, 2023	Federated LearningScene Classification	CodeCode Available
Compressing audio CNNs with graph centrality based filter pruning	May 5, 2023	Acoustic Scene ClassificationAudio Classification	—Unverified
Unsupervised Improvement of Audio-Text Cross-Modal Representations	May 3, 2023	Acoustic Scene ClassificationClassification	CodeCode Available
Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification	May 1, 2023	ClassificationRobust classification	—Unverified
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images	Apr 21, 2023	ClassificationComputational Efficiency	CodeCode Available
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence	Apr 13, 2023	Few-Shot LearningScene Classification	—Unverified
Efficient CNNs via Passive Filter Pruning	Apr 5, 2023	Computational Efficiencyimage-classification	—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