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
Environmental Sound Classification with Parallel Temporal-spectral Attention	Dec 14, 2019	Acoustic Scene ClassificationAudio Classification	—Unverified
Deliberative Explanations: visualizing network insecurities	Dec 1, 2019	AttributeObject Recognition	CodeCode Available
A Discriminative Learned CNN Embedding for Remote Sensing Image Scene Classification	Nov 28, 2019	ClassificationGeneral Classification	—Unverified
In-domain representation learning for remote sensing	Nov 15, 2019	Image ClassificationMulti-Label Image Classification	CodeCode Available
Characterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting	Nov 10, 2019	Acoustic Scene ClassificationGeneral Classification	—Unverified
Centroid Based Concept Learning for RGB-D Indoor Scene Classification	Nov 1, 2019	ClassificationClustering	CodeCode Available
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification	Oct 30, 2019	Domain AdaptationFew-Shot Image Classification	CodeCode Available
Cross-task pre-training for on-device acoustic scene classification	Oct 22, 2019	Acoustic Scene ClassificationClassification	—Unverified
Acoustic Scene Classification Based on a Large-margin Factorized CNN	Oct 14, 2019	Acoustic Scene ClassificationClassification	—Unverified
Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification	Sep 30, 2019	ClassificationGeneral 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