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

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Showing 431–440 of 453 papers

Title	Date	Tasks	Status
Semantic-Aware Scene Recognition	Sep 5, 2019	Scene ClassificationScene Recognition	CodeCode Available
Training neural audio classifiers with few data	Oct 24, 2018	Acoustic Scene ClassificationGeneral Classification	CodeCode Available
Local semantic enhanced convnet for aerial scene recognition	Jul 8, 2021	Aerial Scene ClassificationImage Classification	CodeCode Available
Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embedding	Mar 1, 2017	feature selectionGraph Embedding	CodeCode Available
Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systems	May 28, 2021	Acoustic Scene ClassificationQuantization	CodeCode Available
AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive Learning	Nov 21, 2023	Acoustic Scene ClassificationAudio captioning	CodeCode Available
Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images	Jan 2, 2023	Decision MakingScene Classification	CodeCode Available
Low-Complexity Acoustic Scene Classification Using Parallel Attention-Convolution Network	Jun 12, 2024	Acoustic Scene ClassificationData Augmentation	CodeCode Available
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge	May 3, 2025	Acoustic Scene ClassificationScene Classification	CodeCode Available
Neural Embedding Compression For Efficient Multi-Task Earth Observation Modelling	Mar 26, 2024	Earth ObservationScene Classification	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