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	Hype
Cross-task pre-training for on-device acoustic scene classification	Oct 22, 2019	Acoustic Scene ClassificationClassification	—Unverified	0
Acoustic Scene Classification Based on a Large-margin Factorized CNN	Oct 14, 2019	Acoustic Scene ClassificationClassification	—Unverified	0
Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification	Sep 30, 2019	ClassificationGeneral Classification	—Unverified	0
Acoustic scene analysis with multi-head attention networks	Sep 16, 2019	Acoustic Scene ClassificationGeneral Classification	CodeCode Available	0
Receptive-field-regularized CNN variants for acoustic scene classification	Sep 5, 2019	Acoustic Scene ClassificationClassification	CodeCode Available	1
Semantic-Aware Scene Recognition	Sep 5, 2019	Scene ClassificationScene Recognition	CodeCode Available	0
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification	Sep 4, 2019	Acoustic Scene ClassificationBIG-bench Machine Learning	CodeCode Available	0
Online Sensor Hallucination via Knowledge Distillation for Multimodal Image Classification	Aug 28, 2019	ClassificationDecision Making	—Unverified	0
Multiple instance dense connected convolution neural network for aerial image scene classification	Aug 22, 2019	General ClassificationScene Classification	—Unverified	0
Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings	Aug 18, 2019	ClassificationGeneral Classification	—Unverified	0

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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