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 201–210 of 453 papers

Title	Date	Tasks	Status
A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer	Oct 16, 2021	Acoustic Scene ClassificationScene Classification	CodeCode Available
ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition	Oct 14, 2021	feature selectionScene Classification	—Unverified
Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN	Oct 12, 2021	Adversarial RobustnessFoveation	—Unverified
An evaluation of data augmentation methods for sound scene geotagging	Oct 9, 2021	Acoustic Scene ClassificationClassification	—Unverified
Visually Exploring Multi-Purpose Audio Data	Oct 9, 2021	Acoustic Scene ClassificationClassification	—Unverified
SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification	Oct 9, 2021	graph constructionGraph Matching	—Unverified
Generalized Scene Classification from Small-Scale Datasets with Multi-Task Learning	Oct 8, 2021	ClassificationMulti-Task Learning	CodeCode Available
Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations	Oct 4, 2021	Acoustic Scene ClassificationFairness	—Unverified
Towards Robust Domain Generalization in 2D Neural Audio Processing	Sep 29, 2021	Acoustic Scene ClassificationDomain Generalization	—Unverified
An embarrassingly simple comparison of machine learning algorithms for indoor scene classification	Sep 25, 2021	BIG-bench Machine LearningClassification	CodeCode Available

Show:10 25 50

← PrevPage 21 of 46Next →

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